Bibliographic record
Abstract
Abstract | This artist-response essay examines some ethical and aesthetic contours of media-theoretical hoaxes (and of a hoaxing media theory). I accomplish this through an exploratory reflection upon my own experiences and dilemmas as a media hoax artist, a vocation that has been influenced by Harold Adams Innis’s “authentic” scholarly persona as well as by McLuhan’s “probing” methods. Whereas recent work in the field of hoax art has tended to rely on the eventual text-bound revelation of the truth of the situation, my McLuhanite method aims rather towards magic and mediation.Résumé | Cet essai et réponse d’artiste examine quelques contours éthiques et esthétiques des canulars médiatiques (et d’une théorie des canulars médiatiques). J’accomplis cela à travers une réflexion exploratoire sur mes propres expériences et dilemmes en tant qu’artiste de canular médiatiques, une vocation qui a été influencée par la personnalité académique « authentique » d’Harold Adams Innis ainsi que par les méthodes « exploratoires » de McLuhan. Alors que les travaux récents dans le domaine de l’art du canular ont eu tendance à dépendre de la révélation éventuelle de la vérité de la situation, ma méthode McLuhanite s’appuie plutôt sur la magie et la médiation.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".