Public Deception as Ideological and Institutional Critique: On the Limits and Possibilities of Academic Hoaxing
Bibliographic record
Abstract
Background Through an exploration of two influential academic hoaxes, the Sokal Affair and the “Grievance Studies” hoax, this article explores the constraints and possibilities of academic hoaxing in the articulation of institutional critique through a discussion of academic integrity and ethical forms of deception. Analysis In this article, hoaxes are cast as operating on a continuum with other covert forms of deception in academic publishing (fraud, data fabrication, misconduct). Far from producing constructive outcomes, these interventions serve as flashpoints for stirring up discipline-based anxieties and ideologically motivated attacks. Conclusion and implications These forms of public deception can illuminate how to reform or re-envision areas of academia that are compromising the health and vitality of academic research.
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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 | 0.057 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.022 | 0.195 |
| Scholarly communication | 0.034 | 0.027 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".