Bibliographic record
Abstract
One of the challenges (dare I say frustrations?) of academic publishing is how glacially slow it seems when compared with the immediacy of Twitter feeds, Facebook pages, Wordpress blogs, Instagram updates, and related digital media platforms. Although the TRIC/RTAC editorial team does its best to move promising articles through the peer review process quickly and efficiently, the time between submission and publication is often twelve months (or longer), depending on reviewers’ comments and authors’ commitments, not to mention the idiosyncrasies of the publication schedule and related considerations. Why publish then? For me, one of the most important features of academic publishing is the way it allows ideas to grow over time—to become richer, deeper, sharper through the processes of peer review and revision. Like wine (or beer, depending on your tastes), thought often improves through fermentation. As an academic journal, our goal is not to pump out streams of information into the perpetually hungry mediasphere but rather to publish compelling, rigorously researched, and persuasively argued articles that advance the discipline of theatre and performance studies. And this takes time. Time, of course, is many things: ticking, relentless, linear, non-linear, looping, transformative, destructive, political, precious. In the interests of time, then, I hope that you will grant us some of yours and indulge in a few hours of reading (or more).
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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.104 | 0.059 |
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".