Bibliographic record
Abstract
À la tombée de la nuit dans l’archipel de Hong Kong, une écologie de spectres – tantôt fantômes (gui 鬼), ancêtres et dieux – se manifestent à la lueur de brasiers allumés en leur mémoire. Cette écologie de spectres et les gestes de soin pyrotechniques qui l’animent, tendent à disparaître sous la pression de l’urbanisation et de campagnes écologiques. Dans cet article, je m’attarde sur ce et ceux que l’extinction de ces feux risque de faire disparaître. Je propose de considérer que ce qui disparait est moins un certain obscurantisme – tel que les campagnes d’éducation du gouvernement, promouvant des enterrements et funérailles écologiques le laissent suggérer – mais davantage un rapport à la nuit, au feu, au récit. Ce qui vient à disparaître en même tant que ces feux, c’est une perspective, celle des morts, qui confronte les vivants à leur propre finitude et devenirs post mortem.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".