Bibliographic record
Abstract
In our business, there are often hard acts to follow. As new editor for Theatre Research in Canada / Recherches théâtrales au Canada, I certainly have the most humbling of roles following in the footsteps of a long line of first-class editors, not least of which is my immediate predecessor, Bruce Barton, who, in addition to continuing the excellence of TRIC content for the past four years, oversaw the complete re-visioning of the look of the journal.All of us in this field who depend on TRIC owe Bruce our deepest appreciation and gratitude for the work he has accomplished. Fortunately for all of us Bruce is not leaving the journal, but will be assuming the tasks of Executive Editor to keep everything running smoothly and to ensure the continued excellence of the journal. My role is also made possible only through the seemingly inexhaustible contributions of the office assistants, this year Birgit Schreyer Duarte and Barry Freeman. Without their superior skills and formidable dedication, the journal could not see the light of day. Also keeping this show on the road is house translator, Sonya Malaborza, French-language associate editor, Louise Ladouceur, and the managing editor, Stephen Johnson, who as chair of the management board gives the journal the security of consistent and transparent governance.
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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.188 | 0.182 |
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".