Bibliographic record
Abstract
This work represents a commitment of many years, as life took tremendous and varied turns, occasionally through deep valleys, but more often over mountaintops.It would have been a project easily abandoned had it not been for the support from and the conversations with those who travelled with me.I give thanks for the brilliant teaching of the late George P. Schner, S.J., whose encouragement and theological acumen inspired me alternately to change paths, to press forward, and to start again.Joseph L. Mangina nurtured this project from its earliest stages with a rigour and a generosity that far exceeded obligation and expectation.Harold Wells showed great wisdom and kindliness in his capacity as co-supervisor of the thesis that preceded this book.Doug Harink and George Vandervelde offered invaluable counsel and encouragement.I thank the anonymous reviewers, who also had wonderful suggestions for the manuscript that I hope I have heeded in some form.Joanne McWilliam of Editions SR offered no small measure of assistance as I navigated this new experience of authorship.The editorial team at Wilfrid Laurier University Press was entirely helpful and judicious in preparing this manuscript.Fifteen years ago, I took a course in Canadian Church History with an outstanding scholar and teacher who first nudged me in the direction of
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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 | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.307 | 0.209 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".