Simulated Sense‐Making or Social Knowledge? Artificial Intelligence and the Boundaries of Representation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT This article examines whether AI‐generated texts—such as stories produced by large language models (LLMs)—can be considered social representations as defined by social representation theory. This paper argues that AI‐generated outputs simulate communicative behaviour without participating in social processes of meaning‐making. Although these texts contain familiar symbols, metaphors or narrative structures, they lack dialogical co‐construction, intentionality and embeddedness in cultural practices. This paper introduces the concept of quasi‐agents to capture the distinctive role that AI systems occupy in social interactions: entities perceived as social interlocutors, despite lacking genuine intentionality or social consciousness. This conceptual innovation extends social representation theory's analytical vocabulary, facilitating clearer distinctions between socially constructed meanings and algorithmically generated simulations. Misidentifying machine‐generated texts as genuine social knowledge risks eroding the dialogical foundations of public discourse, particularly in education, media and policy contexts. Ultimately, meaning‐making remains fundamentally a human and collective endeavour—one that AI may mirror but not originate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it