O is for open (as well as optimal, operable, optimistic, organic)
Bibliographic record
Abstract
Much as we might like to think of the academy as an enlightened domain of pure knowledge creation, it is inextricably linked to financial and corporate influences. The business of academic publishing is a complex ecosystem of actors, processes, expectations, and perversions. Many of us have encountered—indeed, have reinforced—such entanglements. Very often our academic success depends on learning the rules of engagement and then following them. We research, we write, we publish; we review, we critique, we edit. We make our textual submissions and pay our subscription fees, whether directly to a journal or indirectly through our participation in the institutions, organizations, and libraries to which we are connected. But this system, when we start to unpack it, can present some pretty nefarious effects. Research papers published in for-profit journals are not easily and freely accessible to those outside of institutional life. Yet these papers are generally produced by people with access to public funding, either from research councils or educational institutions. By paying for journal articles that sit behind paywalls, we are effectively transferring tax revenue into the pockets of private corporations. Of course, for those who can’t or don’t want to pay, there are semi- and non-legal options, but even ‘free’ access to PDFs comes with costs (often folded back into commercial publishers’ fee structures).
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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.050 |
| Scholarly communication | 0.026 | 0.021 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.045 | 0.020 |
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".