Bibliographic record
Abstract
Public conversation around generative AI is saturated with the ‘realness question’: is the software really intelligent? At what point could we say it is thinking? I argue that attempts to define and measure those thresholdsmisses the fire for the smoke. The primary societal impact of realness question comes not from the constantly deferred sentient machine of the future, but its present form as rituals of misrecognition. Persistent confusion between plausible textual output and internal cognitive processes, or the use of mystifying language like ‘learning’ and ‘hallucination’, configure public expectations around what kinds of politics and ethics of genAI are reasonable or plausible. I adapt the notion of abductive agency, originally developed by the anthropologist Alfred Gell, to explain how such misrecognition strategically defines the terms of the AI conversation. I further argue that such strategic misrecognition is not new or accidental, but a central tradition in the social history of computing and artificial intelligence. This tradition runs through the originary deception of the Turing Test, famously never intended as a rigorous test of artificial intelligence, to the present array of drama and public spectacle in the form of competitions, demonstrations and product launches. The primary impact of this tradition is not to progressively clarify the nature of machine intelligence, but to constantly redefine values like intelligence in order to legitimise and mythologise our newest machines – and their increasingly wealthy and powerful owners.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.080 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".