Bibliographic record
Abstract
A book of essays like the one in your hands requires gifts of time and the goodwill of a network of academics.I am happy to say that the project has received both from a number of scholars in French and English Canada, the United States and Europe.These essays were stimulated in large part from a three-year standard research grant from the Social Sciences and Humanities Research Council (SSHRC) of Canada.Thank you to Cory Labrecque, who originally encouraged this work, as we tested the basic theses at an American Academy of Religion (AAR) conference together in 2009.Victor Hori also gave early encouragement for the idea.Victor has continued to be helpful with expertise and experience in areas of research that were outside my normal field of study.Elizabeth Smyth showed great enthusiasm for the project from its very beginning and has continued to give careful advice to the project all along.In particular I thank Elizabeth Smyth for extra funding for our demographic work from her SSHRC grant, "Leading Sisters, Changing Times: Women Religious in English Canada in the Post-Vatican II World."Rick van Lier, OP, has been invaluable throughout the project.I especially thank Victor Hori, Elizabeth Smyth, and Rick van Lier for their willingness to tie their networks of scholarship into this one.It has made the experience richer even if the dissonance of methodologies or definitions necessitated more work.One of the most pleasant aspects of this research was work with graduate students at Concordia, McGill, Notre Dame, and Harvard-all of whom proved not only capable in their research, but also cheerfully helpful in the various meetings of our research group.I thank Kathryn Sawyer, Martha Downey, and Lina Verchery for their enthusiasm throughout.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.186 | 0.091 |
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".