Une anthropologie quichottienne? Note sur l’extravagance et la pige littéraire1
Bibliographic record
Abstract
Don Quichotte (le célèbre personnage de Cervantès) souffrit d’avoir trop lu de romans de chevalerie ce qui altéra profondément sa perception de la réalité. En s’appuyant sur cette figure romanesque extravagante, cette brève note exploratoire à pour but d’entamer la réflexion sur certaines implications et problématiques propres à une pratique appelée ici pige littéraire. Cette pratique, consistant à puiser dans le corpus littéraire des manières de voir, percevoir et concevoir le monde, est abordée à l’aide de trois exemples choisis dans des oeuvres récentes de James A. Boon, Vincent Crapanzano et Michael Taussig. En tissant une analogie entre cette pratique et l’ouvrage de Cervantès, cet article survole certains de ces aspects tout en soulevant quelques questions : Certains anthropologues souffrent-ils du syndrome de Quichotte? Lisent-ils trop de romans? Font-ils un usage abusif de ceux-ci dans leurs travaux? Cette fascination pour la littérature fausse-t-elle conséquemment leur perception de la réalité (ou l’altère-t-elle simplement)?
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".