Bibliographic record
Abstract
Every discipline has its foundational terms, those words that practitioners use to name what they study, or how they study, or why that study is valid. These terms often go unscrutinized when a discipline is up and running, but in the formative stages of a discipline and in periods of contention or crisis they often become subject to intensive criticism and attempts at redefinition. Challenging foundational terms is no simple task. Because they are foundational, they are difficult to do without, even by those who would reject them. They are often used or assumed by those who question them, and after intensive questioning is done they often simply recur, even in their most unquestioned, naive form. The “foundational” nature of foundational terms, one begins to see, is not just a matter of a basic organizing concept or a description of some natural process, but of a term that is invested with aspirations of different sorts. The term also offers authorization to interpret, enacts a wish, suggests a cultural utopia, or puts in place a political program. The density of functions found in foundational terms guarantees that even the most questioned term will return.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.072 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".