Bibliographic record
Abstract
During the 2007 provincial election campaign, Conservative party candidateJohn Tory proposed extending government funding to all faith-based schoolsin Ontario. This was met with strong public and media opposition due to fearsof radicalization and indoctrinating students in religious beliefs considered outdatedand a threat to Canadian norms (particularly with Islamic schools). It iswith this anecdote that editors Graham P. McDonough, Nadeem Memon, andAvi L. Mintz introduce Discipline, Devotion, and Dissent: Jewish, Catholic,and Islamic Schooling in Canada. As they note, the impassioned debate surroundingTory’s election promise, as well as his ensuing loss, indicate that religiouseducation is a particularly contentious topic in an increasingly secularsociety. And yet there is surprisingly little scholarly literature on this topic.The editors seek to address this gap through this excellent and muchneeded contribution to the field. Focusing solely on Catholic, Islamic, and Jewishschools, which make up the vast majority of Canada’s full-time religiousschools, the editors seek not to provide an overview of religious education, butto address three issues: The schools’ aims and practices, how they “negotiatethe tension between the demands of the faith and the expectation that they educateCanadian citizens,” and how they “respond to internal dissent.” ...
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.025 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".