Permeation of Catholic Identity: Some Challenges for Canadian Catholic Schools - Part 1
Bibliographic record
Abstract
Catholic schools seek to provide an educational vision where the religious dimension is given prominence not just in formal religious education classes but throughout the curriculum and in the wider life of schools. One way of expressing this idea is to argue that Catholic identity should permeate all that is done in the school. This paper, the first in a two part series, argues that while permeation may be a worthwhile goal for Catholic schools to strive for this must be undertaken within the context of a number of significant challenges. These include, most notably, the changing demographics of schools where many parents, students and teachers no longer exhibit high levels of religious commitment. This results in a loss of the critical mass needed for effective collaborative action.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.085 | 0.024 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".