Bibliographic record
Abstract
The ambition of scholarship in the humanities is to systematically understand the human condition in all its aspects and times. To this end, humanists are more apt to interpret specific phenomena than generalize to previously unseen observations. When we consider scholarship as a collective effort, this has consequences. I argue that most of the humanities rely on a distinct social contract. This contract states that interpretive arguments are expected to be plausible and the grounds on which they are made, verifiable. This is the scholarly purpose (albeit not the rhetorical one) of most of what goes in our footnotes, especially references. Reference verification is mostly a virtual act, i.e., it all too rarely happens in practice, yet it is in principle always possible. Any individual scholar in any domain in the humanities can, by virtue of this contract, verify the evidence supporting any argument in a non-mediated way. This is essential to, at the very least, distinguish between solid and haphazard arguments.
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 | 0.026 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.075 |
| Scholarly communication | 0.024 | 0.059 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".