Bibliographic record
Abstract
ist phoned me, asking for my reaction to a story about a kindergarten child in Ontario facing expulsion for hugging and kissing some of his classmates.1 Apparently the parents of the children at the receiving end of his affection were not complaining, but the behaviour was seen as contravening the Ontario Safe Schools Act – protecting children from sexual harassment. Zero tolerance in action! I responded that if the story was accurate, it was an example of “a system gone berserk.” Policies of this sort counteract what we hope to cultivate in schools: caring for one another, applauding differences, and creating community. Zero tolerance policies stem from the culture of fear that pervades many schools today – fear of violence, bullying, and unruly behaviour. The code of conduct is clearly spelled out and if students disobey, the retribution is swift – usually suspension or expulsion. The rules are designed to apply equally to everyone, irrespective of age, gender, cultural background, personal characteristics, parental influence, or school experiences. Under the guise of “equity,” zero tolerance policies are, in fact, inequitable, inhospitable and discriminatory. They contravene what we hold dear as educators and as a society. Further, they are ineffective on a number of fronts. I find the concept of zero tolerance oddly out of place in a public school system and jarring to my sensibilities as an educator. It is much more suited to the culture from which it came – the U.S. military, where conformity and control are paramount. The fact that it found its way into the school
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.098 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.005 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".