Worth More than Many Sparrows: Essays in Honour of Willi Braun
Bibliographic record
Abstract
When it comes to the study of religion, Willi Braun is a paragon of what a methodologically rigorous and epistemologically modest academic ought to look like. Braun’s career began in the 1990s, when he studied among a cadre of other notable graduate students at the Centre for the Study of Religion at University of Toronto—what is often referred to as the “Toronto School.” There, Braun and his comrades maintained a fidelity to a particular methodological ethos: that religion should be studied as a fundamentally human phenomenon and that scholars should examine how the “data” of religions (texts, artifacts, rituals, etc) reveal the interests, concerns, and values of the humans who imbue that same data with something divine or transcendent. The Toronto School’s commitment to this ethos led to the inauguration of the North American Society for the Study of Religion and fostered development of the now-renowned journal Method & Theory in the Study of Religion. Braun was a catalyst in these discipline-changing initiatives and brought them to bear in his own work on antiquity and early Christianities. Yet beyond that, Braun’s career also involved an unwavering commitment to pedagogy, as he selflessly endeavored to pass on his exceptional professional and personal qualities to his students. In an effort to honor Braun’s work and mentorship, this volume is focused on exploring, probing, and theorizing ancient religious data as reflections of human interests and activities.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".