Bibliographic record
Abstract
As every ethnographer knows, in addition to the many blunders every one of us commits in the course of our fieldwork, there exist also graver mistakes when we sense a mistaken regime of reality granted to an entity. It is at those moments, usually the most revealing in the course of our inquiries, when we try to repair broken relations by some innovative move to define the status of the contrasting realities that have been open to misinterpretation. During the last quarter century I have attempted, quite systematically, to increase the number of templates by which the so-called Moderns account for themselves; not, to be sure, in their official representation (they remain staunch adepts of the Object-Subject Operating System and will swear that they are obedient naturalists), but by looking for the many occasions where they express dissatisfaction with such an official view of themselves. What I think I have documented are the protestations by many different people that a skewed template is being used to account for the mode of existence of the agencies that are most attached to them.
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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.058 |
| Scholarly communication | 0.012 | 0.028 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".