A Different Tenor: Glimpses and Critical Reflections
Bibliographic record
Abstract
Abstract: On October 19, 2009, the Toronto School of Theology marked its fortieth anniversary with a luncheon. Reflecting on the speeches at the occasion, the Toronto Journal of Theology extended in a challenging and amicable way an invitation to provide more information on how exactly each of the founding member colleges entered into the collaborative endeavour known simply as tst. Brochures and website information on tst are general and sparse in details on its founding and development. By extending the invitation, we wanted each college to tells its story of how it became part of tst, who would have been the primary player(s) in the member institution, and what were the influences and challenges that added to the impetus in forging the arrangement we have today. From each college we invited a senior faculty member who would have had a hand in the move or was in some other way closely connected to or knowledgeable about it. An outsider's critical-evaluative overview of tst would not only be truly a view from a distance, but would obviate having to identify the first among equals in the arrangement, and well suited as a contribution in the set of narratives. With that in mind, we also invited Professor Richard J. Mouw to ponder tst at forty—whither and why?
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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 | 0.022 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.034 | 0.073 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.010 | 0.031 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".