Exploring a potential connection between religious bullying and religious literacy in Modesto and Montreal public schools
Bibliographic record
Abstract
Bullying is a well-researched phenomenon but bias-based bullying, such as bullying regardingsexual orientation, race, and gender, are only beginning to receive more attention. Religiousbullying, which occurs to individuals based on religious and non-religious bias, is one form ofbias-based bullying that has not been researched in-depth academically. Hence, from myobservations of religious bullying in one public school environment, I explored the potentialconnection between religious bullying and religious literacy to consider how teachers could usereligious literacy as a means to address religious bullying during the school hour. Through aCritical Communicative Methodology, this study surveyed 106 students and interviewed 32participants altogether in Modesto, California and Montreal, Quebec, due to the mandatoryreligious literacy courses in secondary schools in each of these cities. Findings show that theconnection between religious bullying and religious literacy can be positive and negativedepending on the curriculum, teacher attitude, teacher training, and administrative support. Thesocial-ecological framework helps us understand that the lived environment in and outside of theschool is equally important in its influence of religious literacy and religious bullying. Thus, evenwhere a religious literacy course exists, the lived environment can influence teacher or student biastowards religious bullying, regardless of the school curriculum on religious literacy
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".