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🤔 Pulse of the library 2025

🤔 "Does anything survive when we remove the metaphor?" A rewriting experiment that tests whether anthropomorphic AI discourse can be translated into strictly mechanistic language while preserving the phenomena described.

About

This document presents a Critical Discourse Analysis focused on AI literacy, specifically targeting the role of metaphor and anthropomorphism in shaping public and professional understanding of generative AI. The analysis is guided by a prompt that draws from cognitive linguistics (metaphor structure-mapping), the philosophy of social science (Robert Brown's typology of explanation), and accountability analysis.

All findings and summaries below were generated from detailed system instructions provided to a large language model and should be read critically as interpretive outputs—not guarantees of factual accuracy or authorial intent.

Task 1: Metaphor and Anthropomorphism Audit​

About this task

For each of the major metaphorical patterns identified, this audit examines the specific language used, the frame through which the AI is being conceptualized, what human qualities are being projected onto the system, whether the metaphor is explicitly acknowledged or presented as direct description, and—most critically—what implications this framing has for trust, understanding, and policy perception.

V3 Enhancement: Each metaphor now includes an accountability analysis.

1. Software as Colleague (The Assistant Framework)​

Quote: "ProQuest Research Assistant Helps users create more effective searches... [and] explore new topics with confidence."

  • Frame: Software as Human Staff Member
  • Projection: This metaphor projects the human quality of professional assistance, mentorship, and collegial support onto a database retrieval interface. By labeling the software an 'Assistant,' the text projects the capacity for conscious 'helping,' 'guidance,' and 'support' (qualities requiring empathy and intent) onto a probabilistic search algorithm. It implies the system 'knows' the user's needs and 'intends' to aid them, rather than simply processing query tokens against an index. This conflates the mechanistic retrieval of information with the social and intellectual labor of a human research assistant.
  • Acknowledgment: Unacknowledged; presented as a product name and direct description of function.
  • Implications: The 'Assistant' framing is the central rhetorical device of the report's product section. It fundamentally alters trust by suggesting the software has a fiduciary or supportive relationship with the user, rather than a transactional one. It implies the system shares the user's goals (research success) rather than the vendor's goals (engagement metrics). By projecting 'knowing' (the assistant knows the topic), it risks leading users to over-rely on the system's 'confidence'—a term used in the text to describe user feeling but often conflated with statistical probability. This creates a risk where users delegate critical thinking to a system they believe is a 'partner' rather than a tool.

Accountability Analysis:

  • This framing displaces the agency of the actual human researchers and the corporate designers. The 'Assistant' is credited with 'helping,' yet: 1) Clarivate designed the product to maximize reliance on their ecosystem; 2) Library administrators deploy it; 3) Clarivate profits from licensing fees that replace human labor budgets. The agentless construction 'Assistant helps' obscures the decision to replace human instruction with automated retrieval. A more accurate accountability framing would be: 'Clarivate engineers optimized this search interface to surface results that keep users on the platform.'
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2. Interaction as Social Dialogue​

Quote: "Enables users to uncover trusted library materials via AI-powered conversations."

  • Frame: Interface as Interlocutor
  • Projection: This maps the human social practice of 'conversation'—which involves shared context, turn-taking, mutual understanding, and Gricean maxims of cooperation—onto a Command Line Interface (CLI) utilizing natural language processing. It attributes the conscious state of 'listening' and 'responding' to a system that mechanistically parses syntax and generates statistically probable text continuations. It suggests the AI 'understands' the conversation, whereas it only processes the token sequence.
  • Acknowledgment: Unacknowledged.
  • Implications: Framing query-response loops as 'conversations' creates a 'curse of knowledge' effect where users assume the system shares their semantic context. It encourages anthropomorphic trust; humans trust conversational partners who 'speak' fluently. This risks users divulging private data or trusting hallucinations because the 'conversational' tone mimics human certainty. It hides the fact that the system has no memory or awareness of the 'conversation' beyond the immediate context window's token limit.

Accountability Analysis:

  • The 'conversation' frame obscures the architectural decisions made by Clarivate's product teams. Who decided what the 'conversational' guardrails are? Who tuned the system's tone to be authoritative? By framing it as a neutral dialogue, Clarivate hides the 'system prompt' (the hidden instructions given by developers) that constrains what the AI can say. The agency lies with the prompt engineers at Clarivate who scripted the 'personality' of the bot, not the bot itself.

3. Cognition as Mechanical Force​

Quote: "Artificial intelligence is pushing the boundaries of research and learning."

  • Frame: Technology as Pioneer/Agent
  • Projection: This maps the human qualities of ambition, exploration, and physical exertion ('pushing') onto a set of software tools. It attributes agency and intent to the technology itself, suggesting AI has a desire to expand knowledge. This is a form of consciousness projection where the 'will' to discover is located in the code rather than in the human researchers using the code.
  • Acknowledgment: Unacknowledged.
  • Implications: This inevitably framing suggests AI is an autonomous force of nature that cannot be stopped, only adapted to. It implies that 'pushing boundaries' is an inherent property of the software, masking the fact that the 'boundaries' being pushed are often ethical (copyright, privacy) or economic (labor automation). It conflates 'processing more data' with 'expanding the frontiers of knowledge,' inflating the system's epistemic status.

Accountability Analysis:

  • Who is actually 'pushing boundaries'? 1) Clarivate executives pushing for market share; 2) University administrators pushing for efficiency; 3) Tech companies pushing against copyright laws to train models. The sentence attributes this aggressive expansion to 'Artificial Intelligence' (an abstract noun), thereby erasing the specific corporate strategies and legal risks undertaken by Clarivate and its partners to deploy these systems.

4. The Vital/Biological Institution​

Quote: "Pulse of the Library 2025"

  • Frame: Institution as Living Organism
  • Projection: This title metaphor maps the biological function of a heartbeat (vitality, life, rhythmic health) onto the statistical aggregation of survey data. While not directly anthropomorphizing AI, it sets the stage for treating the library ecosystem as a biological entity that can be 'diagnosed' or 'treated' by the report's authors.
  • Acknowledgment: Unacknowledged; used as the report title.
  • Implications: This naturalizes the data. It suggests the report captures the 'natural' state of the field, rather than a constructed narrative based on a specific sample of Clarivate customers. It builds authority—the authors are the 'doctors' feeling the pulse—which prepares the reader to accept their 'prescription' (buying AI tools).

Accountability Analysis:

  • Clarivate is the actor taking the 'pulse.' They designed the survey questions to elicit specific anxieties (budget, staffing) that their products claim to solve. The 'Pulse' metaphor hides the constructed nature of the survey—it's not a neutral biological reading, but a market research instrument designed by a vendor who profits from the 'diagnosis.'

5. The Trusted Partner​

Quote: "Clarivate... A trusted partner to the academic community."

  • Frame: Corporation as Faithful Companion
  • Projection: This maps the human quality of 'trustworthiness' (based on moral character, loyalty, and shared values) onto a publicly traded data analytics corporation. It implies a relationship of mutual care, rather than a contractual vendor-client relationship.
  • Acknowledgment: Unacknowledged.
  • Implications: This is the foundational trust-building metaphor that allows the AI products to be accepted. If the vendor is a 'partner,' their AI is a 'teammate.' It obscures the profit motive; partners share risks, whereas vendors transfer risks (like liability for AI hallucinations) to the client.

Accountability Analysis:

  • Clarivate claims the status of 'partner' while maintaining the legal protections of a vendor. The agency here is entirely corporate: Clarivate's marketing team selected this frame to smooth over the friction of selling expensive, potentially disruptive technology to cash-strapped libraries. It obscures the fact that a 'partner' doesn't usually extract subscription fees during a budget crisis.

6. Cognitive Understanding​

Quote: "Hey, I understand getting a blockbuster result is the very best outcome... But if that comes at the price of manipulating your data..."

  • Frame: Librarian as Conscious Gatekeeper vs. AI as Generator
  • Projection: In this quote, a human librarian uses 'understand' to denote deep, contextual, ethical comprehension. The text contrasts this with AI tools that might facilitate manipulation. However, elsewhere the text claims AI 'helps students assess books' relevance,' implying the AI also 'understands.'
  • Acknowledgment: Unacknowledged collision of terms.
  • Implications: The text relies on a slippage where 'understanding' is deep and ethical when humans do it, but functional and retrieval-based when AI does it ('uncover trusted materials'). By not distinguishing these types of 'understanding,' the text elevates the AI's pattern matching to the level of the librarian's ethical judgment.

Accountability Analysis:

  • The quote correctly identifies the human (librarian) as the agent of ethical reasoning. However, the surrounding text describing AI tools (e.g., 'Web of Science Research Intelligence... support decision-making') attempts to offload this cognitive labor to the software. The accountability analysis here reveals a tension: the text quotes a librarian prioritizing human judgment, while the product catalog section sells tools to automate it.

7. Toolbox Analogy​

Quote: "AI is a great tool, but if you take a screw and start whacking it with a hammer..."

  • Frame: Cognitive Automation as Simple Hand Tool
  • Projection: This maps a generative, non-deterministic, probabilistic system (AI) onto a simple, deterministic, passive physical object (hammer/screw). It strips the AI of its complexity and agency to make it seem manageable.
  • Acknowledgment: Analogy (explicit).
  • Implications: This is a 'containment metaphor.' By calling AI a 'tool' like a hammer, the text minimizes the risks of hallucinations, bias, and autonomous action. A hammer doesn't have a training set; a hammer doesn't hallucinate. This metaphor implies that any error is solely the fault of the 'user' (the carpenter), absolving the tool maker. It hides the 'black box' nature of AI.

Accountability Analysis:

  • This framing serves the vendor (Clarivate) perfectly. If AI is just a 'hammer,' then if it produces bad research (whacks the screw), it's the librarian's fault for not 'upskilling' enough. It erases the responsibility of the engineers who built a 'hammer' that sometimes changes shape or disappears while you're using it.

8. Navigate Complex Tasks​

Quote: "Navigate complex research tasks and find the right content."

  • Frame: Software as Expert Pilot/Guide
  • Projection: Maps the cognitive process of evaluating, synthesizing, and selecting research pathways onto the navigational skills of a pilot or guide. It implies the AI 'knows' the terrain (the literature) and the destination (the answer).
  • Acknowledgment: Unacknowledged.
  • Implications: This projects a 'God's eye view' capability onto the AI. It implies the system has a spatial understanding of the knowledge graph and can 'steer' the user. This obscures the fact that the 'navigation' is merely statistical similarity ranking. It invites the user to relax and let the AI 'drive,' creating epistemic dependency.

Accountability Analysis:

  • Who defines the 'right content'? Clarivate's algorithms prioritize their own indexed content (Web of Science). The 'navigation' is not neutral; it is a guided tour through Clarivate's walled garden. The agency belongs to the product managers who designed the ranking algorithms to favor specific content sources, yet the text attributes the success to the AI's navigational skill.

Task 2: Source-Target Mapping​

About this task

For each key metaphor identified in Task 1, this section provides a detailed structure-mapping analysis. The goal is to examine how the relational structure of a familiar "source domain" (the concrete concept we understand) is projected onto a less familiar "target domain" (the AI system). By restating each quote and analyzing the mapping carefully, we can see precisely what assumptions the metaphor invites and what it conceals.

Mapping 1: Human Staff (Assistant) → Software Interface (LLM/RAG)​

Quote: "ProQuest Research Assistant"

  • Source Domain: Human Staff (Assistant)
  • Target Domain: Software Interface (LLM/RAG)
  • Mapping: Maps the qualities of a junior human colleague (helpfulness, availability, competence, subordination) onto a query interface. It implies the software has the capacity to care about the outcome and 'assist' through understanding intent.
  • What Is Concealed: Conceals the lack of consciousness and moral responsibility. A human assistant can be held accountable for bad advice; a software assistant cannot. It also conceals the 'product' nature of the interaction—the assistant is actually a data extraction tool.
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Mapping 2: Human Social Dialogue → Command Line / Prompt Engineering​

Quote: "AI-powered conversations"

  • Source Domain: Human Social Dialogue
  • Target Domain: Command Line / Prompt Engineering
  • Mapping: Maps the reciprocity, shared context, and social contract of human conversation onto the input/output mechanism of a text generator. Assumes the 'partner' has a memory and a self.
  • What Is Concealed: Conceals the 'stateless' nature of many models (or limited context windows) and the fact that the AI is predicting the next word, not formulating a thought. It obscures the prompt engineering required to make the output coherent.

Mapping 3: Physical/Human Exploration → Data Processing/computation​

Quote: "Pushing the boundaries"

  • Source Domain: Physical/Human Exploration
  • Target Domain: Data Processing/computation
  • Mapping: Maps physical exertion and brave exploration of new territory onto the passive processing of larger datasets. Implies AI has an internal drive to discover.
  • What Is Concealed: Conceals the human labor of the researchers. AI doesn't publish papers or discover drugs; it processes data for humans who do those things. It also conceals the energy consumption (physical costs) of this 'pushing.'

Mapping 4: Biological Organism → Market Research Data​

Quote: "Pulse of the Library"

  • Source Domain: Biological Organism
  • Target Domain: Market Research Data
  • Mapping: Maps the health and vital signs of a living body onto a collection of survey statistics. Implies the data is 'natural' and 'vital.'
  • What Is Concealed: Conceals the bias of the survey methodology. A pulse is an objective fact; a survey is a subjective construction. It hides the commercial intent behind 'taking the pulse.'

Mapping 5: Interpersonal Relationship → Corporate Vendor Contract​

Quote: "Trusted partner"

  • Source Domain: Interpersonal Relationship
  • Target Domain: Corporate Vendor Contract
  • Mapping: Maps the vulnerability and mutual support of a friendship or marriage onto a business transaction. Implies shared destiny.
  • What Is Concealed: Conceals the divergent interests: the library wants to save money; the partner (Clarivate) wants to maximize revenue. It conceals the power asymmetry.

Mapping 6: Human Cognitive/Ethical Comprehension → Pattern Matching/Statistical correlation​

Quote: "Understand getting a blockbuster result"

  • Source Domain: Human Cognitive/Ethical Comprehension
  • Target Domain: Pattern Matching/Statistical correlation
  • Mapping: When applied to AI (in the broader context of 'Research Intelligence'), it maps deep semantic and ethical grasping of a concept onto the statistical weighting of tokens.
  • What Is Concealed: Conceals the fact that AI cannot 'understand' consequences, reputation, or truth—only probability. It obscures the 'Chinese Room' reality of the system.

Mapping 7: Simple Mechanical Object → Complex Probabilistic System​

Quote: "AI is a great tool [like a hammer]"

  • Source Domain: Simple Mechanical Object
  • Target Domain: Complex Probabilistic System
  • Mapping: Maps the predictability and passivity of a hand tool onto a system that is unpredictable and active. Implies complete user control.
  • What Is Concealed: Conceals the agency of the algorithm. A hammer doesn't decide to hit your thumb; an AI can 'decide' to hallucinate a citation. It hides the autonomy of the system.

Mapping 8: Spatial Navigation/Piloting → Information Retrieval/Ranking​

Quote: "Navigate complex research tasks"

  • Source Domain: Spatial Navigation/Piloting
  • Target Domain: Information Retrieval/Ranking
  • Mapping: Maps the visual and spatial awareness of a guide onto the mathematical sorting of database entries.
  • What Is Concealed: Conceals the ranking criteria. 'Navigation' implies finding the 'true' path; 'Ranking' implies a biased sorting based on opaque metrics (citation counts, journal impact factors) that Clarivate owns.

Task 3: Explanation Audit (The Rhetorical Framing of "Why" vs. "How")​

About this task

This section audits the text's explanatory strategy, focusing on a critical distinction: the slippage between "how" and "why." Based on Robert Brown's typology of explanation, this analysis identifies whether the text explains AI mechanistically (a functional "how it works") or agentially (an intentional "why it wants something"). The core of this task is to expose how this "illusion of mind" is constructed by the rhetorical framing of the explanation itself, and what impact this has on the audience's perception of AI agency.

Explanation 1​

Quote: "Generative AI tools are helping learners, educators and researchers accomplish more, with greater efficiency and precision."

  • Explanation Types:

    • Functional: Explains a behavior by its role in a self-regulating system that persists via feedback, independent of conscious design
    • Empirical Generalization (Law): Subsumes events under timeless statistical regularities, emphasizes non-temporal associations rather than dated processes

  • Analysis (Why vs. How Slippage): This explanation frames the AI mechanistically in terms of its output ('efficiency') but agentially in terms of its role ('helping'). The choice of the verb 'helping' suggests a functional role within the educational ecosystem, positioning the AI as a benevolent force that naturally increases output. This obscures the genetic explanation: that these tools were developed by corporations to capture data and subscription fees. It presents the 'efficiency' as a natural law of the technology, rather than a marketing claim.

  • Consciousness Claims Analysis: The passage uses the verb 'helping,' which implies an understanding of the user's goals. While 'helping' is not strictly a consciousness verb like 'knowing,' in this context it attributes a cooperative intent to the system. The text conflates 'processing data faster' (mechanistic) with 'accomplishing more' (teleological/human achievement).

Curse of Knowledge: The authors project their knowledge of the outcome (users completing tasks faster) onto the intent of the system (the AI wants to help).

Mechanistic Reality: The system does not 'help'; it processes input prompts and generates text strings based on probability distributions that minimize loss functions. If the output is useful, the user is helped, but the system did not engage in 'helping.'

  • Rhetorical Impact: By framing the AI as a 'helper,' the text lowers the audience's defense mechanisms. We trust helpers. This framing encourages the audience to view the integration of AI as a net positive for productivity, marginalizing concerns about academic integrity or the displacement of critical thinking skills. It suggests reliability—a helper who causes errors isn't really helping.
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Explanation 2​

Quote: "Artificial intelligence is pushing the boundaries of research and learning."

  • Explanation Types:

    • Intentional: Refers to goals or purposes and presupposes deliberate design, used when the purpose of an act is puzzling

  • Analysis (Why vs. How Slippage): This is a purely agential/intentional framing. 'AI' is the subject, and 'pushing boundaries' is the intentional act. It treats the abstract concept of AI as an actor with a progressive agenda. This obscures the human actors (researchers, companies) who are actually doing the pushing. It frames the technological change as autonomous and inevitable.

  • Consciousness Claims Analysis: This passage attributes a high-level conceptual desire ('pushing boundaries') to a computational method.

Consciousness Projection: It suggests the AI 'knows' where the boundaries are and 'intends' to cross them.

Curse of Knowledge: The author knows that the field is advancing; they project this advancement as an intrinsic property of the tool itself.

Mechanistic Reality: In reality, researchers are training models on larger datasets and refining architectures. The AI models themselves are static files of weights until run; they have no concept of 'boundaries' or 'research.' The 'pushing' is the collective result of human engineering efforts and capital investment.

  • Rhetorical Impact: This framing constructs AI as a powerful, autonomous authority. It creates a sense of inevitability—if the AI is pushing boundaries, libraries must follow or be left behind. It diminishes the agency of the librarians to decide whether they want the boundaries pushed in this specific, corporate-driven direction.

Explanation 3​

Quote: "Summon Research Assistant Enables users to uncover trusted library materials via AI-powered conversations."

  • Explanation Types:

    • Functional: Explains a behavior by its role in a self-regulating system that persists via feedback, independent of conscious design

  • Analysis (Why vs. How Slippage): This explanation focuses on the function of the tool ('enables users to uncover'). It bridges the mechanistic ('AI-powered') and the agential ('conversations'). It frames the how (conversations) as the mechanism for the why (uncovering materials).

  • Consciousness Claims Analysis: The use of 'conversations' is a significant epistemic claim. A conversation requires two conscious interlocutors.

Consciousness Projection: The text implies the AI is a 'knower' capable of holding a conversation, rather than a 'processor' executing a command-line query in natural language.

Mechanistic Reality: The system converts the user's natural language input into vector embeddings, performs a similarity search in a vector database ('uncover'), retrieves relevant text chunks, and feeds them into a generator to synthesize a response. There is no conversation; there is query transformation and retrieval.

  • Rhetorical Impact: This framing creates a false sense of intimacy and trust. If users believe they are having a 'conversation,' they may treat the output as expert advice rather than database retrieval. It elevates the authority of the system from a search engine (which lists possibilities) to an oracle (which gives answers).

Explanation 4​

Quote: "These findings suggest that AI progress is still cautious, but the steady increase shown in the data is not linear."

  • Explanation Types:

    • Dispositional: Attributes tendencies or habits such as inclined or tends to, subsumes actions under propensities rather than momentary intentions

  • Analysis (Why vs. How Slippage): This passage attributes a human emotional disposition ('cautious') to an abstract statistical trend ('AI progress'). It explains the why of the slow adoption data by ascribing a personality trait to the progress itself (or implicitly, the libraries collectively).

  • Consciousness Claims Analysis: While not attributing consciousness to a machine, this anthropomorphizes the process of adoption. It treats 'AI progress' as a hesitant agent.

Mechanistic Reality: 'Cautious progress' is a statistical aggregation of thousands of individual human decisions driven by budget shortages and skepticism.

Concealed Distinction: It hides the specific reasons for the 'caution' (lack of funds, ethical concerns) behind a general dispositional label.

  • Rhetorical Impact: Describing the progress as 'cautious' implies it should be faster or bolder. It subtly critiques the libraries for being too slow, positioning rapid adoption as the normative standard. It frames the 'steady increase' as the inevitable overcoming of this caution.

Explanation 5​

Quote: "Librarians recognize that learning doesn't happen by itself... AI tools... [are] essential for building confidence and competence."

  • Explanation Types:

    • Reason-Based: Gives the agent's rationale or argument for acting, which entails intentionality and extends it by specifying justification
    • Theoretical: Embeds behavior in a deductive or model-based framework, may invoke unobservable mechanisms

  • Analysis (Why vs. How Slippage): This explains the why of training investments: because 'learning doesn't happen by itself.' It frames the adoption of AI tools (specifically training programs) as the rational outcome of this recognition. It links human cognition ('recognize') with the necessity of the tool.

  • Consciousness Claims Analysis: Consciousness Projection: Here, the text correctly attributes knowing to librarians ('recognize'). However, it sets up a dynamic where the solution to the human need for learning is the consumption of AI products/literacy.

Curse of Knowledge: The text assumes that 'confidence' in AI is the goal, conflating 'knowing how to use the tool' with 'understanding the tool's nature.'

Mechanistic Reality: The 'confidence' mentioned is likely familiarity with the interface, not a deep technical understanding of the transformers underneath.

  • Rhetorical Impact: This framing establishes the report's authors (Clarivate) as the experts who align with the librarians' own reasoning. It validates the librarians' wisdom ('Librarians recognize...') to sell them the solution (upskilling/products). It makes the purchase of AI services seem like a responsible, reason-based decision rather than a capitulation to vendor pressure.

Task 4: AI Literacy in Practice - Reframing Anthropomorphic Language​

About this task

This section proposes alternative language for key anthropomorphic phrases, offering more mechanistic and precise framings that better reflect the actual computational processes involved. Each reframing attempts to strip away the projections of intention, consciousness, or agency that are embedded in the original language.

V3 Enhancement: A fourth column addresses human agency restoration—reframing agentless constructions to name the humans responsible for design and deployment decisions.

Original Anthropomorphic FrameMechanistic ReframingTechnical Reality CheckHuman Agency Restoration
Navigate complex research tasks and find the right content.The software executes multi-step query expansions to retrieve and rank database entries based on statistical relevance to the user's input.The system does not 'navigate' or 'find' in a conscious sense; it computes similarity scores between the user's prompt vector and the database's document vectors.Clarivate's search algorithms filter and rank results to prioritize content within their licensed ecosystem.
ProQuest Research Assistant Helps users create more effective searches... with confidence.The ProQuest search interface automatically refines user queries using pattern matching to surface results with higher statistical probability of relevance.The model does not 'help' or possess 'confidence'; it generates tokens based on training data correlations that optimize for specific engagement metrics.Clarivate's product team designed an interface that prompts users to rely on algorithmic sorting rather than manual keyword construction.
Uncover trusted library materials via AI-powered conversations.Retrieve indexed documents using a natural language query interface that formats outputs as dialogue-style text.The system does not 'converse'; it parses input syntax to generate a statistically likely text response containing retrieved data snippets.Clarivate engineers designed the interface to mimic human dialogue, obscuring the mechanical nature of the database query.
Artificial intelligence is pushing the boundaries of research and learning.The deployment of large-scale probabilistic models is enabling the processing of larger datasets, altering established research methodologies.AI does not 'push'; it processes data. The 'boundaries' are changed by human decisions to accept probabilistic outputs as valid research products.Tech companies and university administrators are aggressively integrating automated tools to increase research throughput and reduce labor costs.
Web of Science Research Assistant... Navigate complex research tasks.Web of Science Query Tool... Automates the retrieval and ranking of citation data.The tool processes citation graphs; it does not 'navigate' tasks, which implies an understanding of the research goal.N/A - This quote describes computational processes without directly displacing human responsibility (though 'Assistant' is the displacement).
Libraries... address the AI evolution as not a question of 'if', but 'how'.Library administrators are deciding how to integrate algorithmic tools, treating their adoption as a foregone conclusion.N/A - this quote addresses policy, not mechanism.Vendors and policymakers have framed AI adoption as inevitable to pressure library directors into purchasing decisions, limiting their power to refuse the technology.
AI is a great tool, but if you take a screw and start whacking it with a hammer...Generative models are powerful statistical instruments, but applying them to tasks requiring factual determinism yields error-prone results.AI is not a simple 'tool' like a hammer; it is a complex, non-deterministic system that modifies its own processing weights (during training) and generates variable outputs.Users must evaluate the suitability of probabilistic models for specific tasks, a responsibility often obscured by vendors marketing them as universal solutions.
Clarivate... A trusted partner to the academic community.Clarivate... A major vendor of data analytics and workflow software to academic institutions.N/A - Policy/Business claim.Clarivate executives position the company as a 'partner' to secure long-term contracts, obscuring their primary accountability to shareholders rather than libraries.

Task 5: Critical Observations - Structural Patterns​

Agency Slippage​

The text systematically oscillates between treating AI as a passive 'tool' (the hammer/screw analogy) and an active 'agent' (the Research Assistant/Partner). This slippage is strategic: the mechanical 'tool' framing is used to minimize fear (it's just a hammer, you are in control), while the agential 'assistant' framing is used to maximize value (it can 'help,' 'understand,' and 'navigate').

The slippage typically flows from Mechanical -> Agential. The text begins with data and 'tools,' but culminates in 'Assistants' that 'push boundaries.' This creates a 'bait and switch' on agency.

Crucially, agency is removed from human actors. The text repeatedly uses agentless constructions like 'AI adoption... continues apace' or 'budget constraints... represent the greatest concern.' Who set the budgets? Who decided to adopt?

The 'curse of knowledge' is evident when the authors attribute the 'pushing of boundaries' to the AI itself. The authors know the field is moving fast (their own knowledge), and they project this momentum onto the machine, claiming it is doing the pushing. This validates the agential claim: 'We found the AI pushing boundaries,' effectively treating a corporate marketing strategy as an empirical discovery of machine behavior.

Metaphor-Driven Trust Inflation​

Trust is the central commodity being traded in this text, explicitly invoked in product descriptions ('trusted partner,' 'confidence,' 'trusted library materials'). The text constructs 'Relation-Based Trust' (sincerity, partnership, care) toward a system that only warrants 'Performance-Based Trust' (reliability, uptime).

The 'Assistant' and 'Conversation' metaphors are the primary vehicles for this. We trust assistants because they have a fiduciary duty to us; we trust conversational partners because of social contracts. By applying these frames to a vendor's software, Clarivate encourages librarians to extend human-level trust to a statistical product.

Consciousness language acts as a trust signal. Claiming the AI 'understands' suggests it is a competent social actor. If the text merely said the AI 'calculates probability,' the trust would be purely technical (and lower, given the hallucination risks). The text manages failure risks by reverting to the mechanical frame ('it's just a tool') when discussing errors, but uses the agential frame ('it helps') when discussing success. This asymmetry protects the 'brand' of the AI.

Obscured Mechanics​

The text is a masterclass in obscuring the material and economic realities of AI through high-level metaphors.

Economic Realities: The 'Partner' metaphor conceals the extractive nature of the relationship. Clarivate is selling solutions to problems (information overload) that their own metrics (impact factors) helped create. 'Name the corporation' reveals that Clarivate benefits from the 'inevitability' narrative.

Technical Realities: The 'Assistant' metaphor hides the RAG (Retrieval-Augmented Generation) architecture. It implies a cognitive mind, hiding the fact that the 'knowledge' is entirely dependent on the indexed proprietary data. It creates an illusion of infinite wisdom, when it is actually a 'walled garden' query tool.

Labor Realities: The phrase 'Streamlining administrative processes' (mechanical) hides the displacement of human library workers. The 'Research Assistant' product directly competes with entry-level library roles. The metaphor of 'helping' erases the reality of 'replacing.'

Consciousness Obscuration: By claiming the AI 'knows' or 'navigates,' the text hides the lack of ground truth. It suggests the AI has verified the information, whereas it has only statistically correlated it.

Context Sensitivity​

Anthropomorphism is not evenly distributed.

Intensity Zones: The Product Section (pages 27-28) is the epicenter of agential language ('Assistants,' 'Conversation,' 'Partner'). The Survey Findings (pages 9-16) are more mechanical and data-driven ('adoption,' 'implementation'). This suggests that marketing requires anthropomorphism, while analysis permits mechanism.

Audience Positioning: When addressing 'Senior Librarians' (decision-makers), the text focuses on 'strategy' and 'efficiency' (agential results). When addressing the general workforce, it uses 'tools' and 'literacy' (mechanical means).

Capability/Limitation Asymmetry: Capabilities are agential ('The AI pushes boundaries'). Limitations are environmental ('Budget constraints,' 'Privacy concerns'). The AI is never the agent of a mistake; 'privacy' is a passive concern, not an active violation by the model. This distribution insulates the AI agent from blame while crediting it with progress.

Accountability Synthesis​

Accountability Architecture

This section synthesizes the accountability analyses from Task 1, mapping the text's "accountability architecture"—who is named, who is hidden, and who benefits from obscured agency.

The text constructs a perfect 'Accountability Sink.'

The Architecture:

  1. Villains: Abstract forces ('Budgets,' 'Geopolitics') and passive limitations ('Lack of expertise').
  2. Heroes: The AI ('Assistant,' 'Partner') and the Vendor (Clarivate).
  3. Victims/Gatekeepers: The Librarians, who are burdened with the responsibility to 'upskill' and 'adapt.'

Displaced Responsibility: When the text says 'AI is pushing boundaries,' it erases the executives at Clarivate pushing for market saturation. When it says 'Librarians face budget constraints,' it erases the university administrators and state legislators cutting funds.

Liability Implications: If the AI is an 'Assistant' that 'helps,' but the human librarian is the 'Gatekeeper,' then any failure is the librarian's fault for not keeping the gate shut. The metaphor shifts liability from the manufacturer of the faulty tool to the user of the tool. If the AI was framed as a 'product,' product liability would apply. As a 'partner,' it is a shared risk.

Name the Actor: If we renamed 'AI' to 'Clarivate's algorithm,' the sentence 'AI pushes boundaries' becomes 'Clarivate's algorithm pushes boundaries,' which immediately invites scrutiny: Pushes them where? For whose benefit? The agentless construction serves to naturalize corporate expansion as technological evolution.

Conclusion: What This Analysis Reveals​

The Core Finding

The dominant metaphorical system in Clarivate's 'Pulse of the Library 2025' report is the framing of AI as Collaborative Staff ('The Assistant'). This pattern is supported by the auxiliary metaphors of Interface as Conversation and Corporation as Partner. These patterns function systematically to elevate a software product to the status of a colleague. The 'Assistant' metaphor is load-bearing; without it, the products are merely search filters. This system relies on a consciousness architecture that attributes 'knowing' and 'understanding' to the software, which is a necessary precondition for treating it as a partner. You do not partner with a database; you partner with a knower. By establishing the AI as a 'knower' (through 'conversational' ability), the text validates the 'Assistant' frame.

Mechanism of the Illusion:​

The 'illusion of mind' is constructed through a subtle bait-and-switch of agency. The text begins with the harmless imagery of 'tools' (hammers), engaging the audience's desire for control. Once the reader feels safe, it slides into high-intensity anthropomorphism ('Assistants,' 'Partners,' 'Conversations'). This temporal structure disarms critical skepticism. The 'curse of knowledge' plays a pivotal role here: the authors, knowing the utility of the system, project intent onto it. They conflate 'this tool allows you to find X' with 'this tool helps you find X.' This slight verbal shift creates the illusion of a shared goal, masking the mechanical reality of token prediction. The illusion is sealed by the promise of 'confidence'—transferring the machine's statistical probability into the user's emotional certainty.

Material Stakes:​

Categories: Economic, Institutional, Epistemic

These metaphors have concrete, high-stakes consequences.

Economic/Institutional: By framing AI as an 'Assistant,' the text legitimizes the substitution of labor. If a 'Research Assistant' software license costs $20k and a human employee costs $60k, the metaphor implies they perform equivalent functions, justifying the defunding of human roles. Clarivate (the winner) captures the budget that previously went to human staff (the losers).

Epistemic: The 'Conversation' and 'Navigation' metaphors encourage users to trust the AI's outputs as verified knowledge rather than probabilistic retrieval. This shifts epistemic practice from verification (checking sources) to consumption (trusting the partner). This degrades the information literacy of the academic community, making them vulnerable to subtle hallucinations and bias.

AI Literacy as Counter-Practice:​

Reframing this language is an act of resistance against the erasure of labor and the commodification of knowledge. Replacing 'The AI Assistant helps you' with 'The Clarivate Query Tool retrieves data' strips the false veneer of care. It reminds the librarian that they are using a commercial product, not collaborating with a colleague. Correcting 'Conversations' to 'Command Loops' re-establishes the epistemic burden on the human user to verify outputs, countering the 'drift' toward automated complacency. This practice forces a recognition of the accountability gap: it highlights that if the 'Assistant' lies, there is no one to fire, but if the 'Tool' fails, the manufacturer is liable. Adopting this precision resists the corporate strategy of diffusing liability through anthropomorphism.

Path Forward​

The discourse future in library science faces a fork. In one future, we adopt the Anthropomorphic Norm, accepting 'Assistants' and 'Partners.' This future prioritizes ease of use and corporate integration but risks a collapse of critical literacy and a transfer of library sovereignty to vendors. In the alternative Mechanistic Future, institutions mandate 'Product Precision': journals and reports must describe AI as 'probabilistic text generators' and 'retrieval systems.' This future maintains epistemic clarity and clear liability lines but loses the intuitive appeal of the 'helper' narrative. To support this, library associations could demand 'Agency Disclaimers' on all AI products, requiring vendors to state: 'This system processes data; it does not know, understand, or partner.' We must choose whether we want comfortable illusions or difficult, precise realities.

Extended Processing Summary​

Gemini's Processing Steps
What you are about to read…

Gemini refers to the text below as "thought summaries." This is an overt consciousness projection because 'intentions' are hallmarks of a conscious mind that 'knows' what it is doing and why. The concealed mechanistic process is probabilistic text generation. Treat this as just another rhetorical artifact—a way of making the model's processing legible. The first-person framing of these "thought summaries" is a presentation choice for the user-facing output, not a window into "real" thoughts. These are computational artifacts, not cognitive reports from a quirky, curious or conflicted mind.

Defining the Scope

I've initiated the analysis by clearly defining the project's scope. I'm focusing intently on metaphor, anthropomorphism, and accountability within the AI discussions in the "Pulse of the Library 2025" report. This narrowed focus is vital for a targeted and insightful analysis.

Developing the Methodology

I've been refining my approach to structure this project. I'm building on cognitive linguistics, Brown's typology, and insights from the FrameWorks Institute. The project will now consist of a detailed four-task structure, focusing on both the initial audit of metaphors and the deeper structural analysis. I'm aiming for a clear JSON output, ensuring a concise and valid representation of my findings. I'm focusing on ensuring valid JSON output.

Investigating Metaphors & Accountability

I'm now diving into the source text, "Pulse of the Library 2025," to identify key metaphors and potential accountability concerns. The report's themes include AI inevitability, "partnerships," and the framing of AI as a tool or colleague. Notable metaphors I've found are "assistant," "pulse," "tool," and "partner." I'm also examining how accountability is framed, particularly through passive voice and the role of "budgets" as agents. I'm moving forward with a detailed metaphor audit.

Exploring Recent Metaphors

I've been analyzing the metaphors present in the text more deeply. I've found AI is frequently depicted as an agent striving ("pushing the boundaries") or a collaborator ("helping learners"). The product names themselves ("Research Assistants") position AI as a staff member or colleague. The use of "uncover" portrays AI as an interlocutor in a conversation. AI is also framed as a guide or navigator, which further explores its role. The recent additions of "Trusted partner" and "understanding the challenges" are useful for understanding some conflations. "Digital Librarian" as a "prophet" is also relevant.

Analyzing AI's Agency

I'm now in the midst of analyzing specific passages, focusing on the shifting portrayal of AI. Phrases like "Enables users to uncover..." and "AI progress is still cautious..." reveal how agency is attributed to the technology. The reframing of terms like "Assistant" to "Query Interface" is also underway, highlighting the shift from anthropomorphism to a more technical description. The agency-related observations and mechanical obfuscation are providing interesting insights, along with context sensitivity.

Analyzing Conclusion Draft

I've been working on structuring the conclusion. It highlights the discrepancy between the report's promotional language and the cautious survey data. Accountability is framed as a struggle against "budgets," with Clarivate presented as a solution, and librarians as "gatekeepers." This leads me to draft specific patterns: the "Assistant" framework, the slide from "Tool" to "Teammate," the stakes involving budget allocation, the counter-practice of reframing "Assistant," and differing visions for the future. I'm focusing on ensuring clarity and adherence to the word count, especially in the critical observations.

Crafting the JSON

I'm now fully immersed in the JSON construction phase. My primary focus is on meticulously mapping the identified metaphors, their sources and targets, along with detailed explanations. I'm taking care to highlight the "Research Assistant" metaphor, which is central to the product names and, in turn, the marketing. I'm also ensuring that the "Accountability Sink" related to budgets and geopolitics is clearly represented. Currently working on properly quoting to maintain the JSON structure.

Drafting JSON Details

I'm presently building the specific entries for the JSON output. I've begun drafting detailed analyses for each identified metaphor, ensuring clear and concise representations. The "Assistant" framework, including related metaphors like "Conversation," is a priority, as is the role of the "Pulse" metaphor. I'm making sure to capture the accountability concerns, focusing on the agency given to AI versus librarians. It's useful to draft the mental model first.

Finalizing JSON Entries

I'm presently building the specific entries for the JSON output. I've begun drafting detailed analyses for each identified metaphor, ensuring clear and concise representations. The "Assistant" framework, including related metaphors like "Conversation," is a priority, as is the role of the "Pulse" metaphor. I'm making sure to capture the accountability concerns, focusing on the agency given to AI versus librarians. Currently I'm developing a framework for mapping how the $50k figure is used to justify austerity by using the "Research Assistant (Human)" metaphor, which is being used to make a false equivalency.

Analysis Provenance

Run ID: 2025-12-21-pulse-of-the-library-2025-metaphor-gbfvj5 Raw JSON: 2025-12-21-pulse-of-the-library-2025-metaphor-gbfvj5.json Framework: Metaphor Analysis v6.3 Schema Version: 3.0 Generated: 2025-12-21T23:41:19.133Z

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