Bibliographic record
Abstract
Pick a point and enter, " she said.Good advice, I thought.The speaker at the front of the room was giving advice on how to start a project that needed starting.So I picked a point.I had been thinking for some time about LGBTQ students in schools: How could I help?What needed to be done?What was there to do that wasn't already being done?At the same time, many schools, teachers, and parents in Ontario were engaging in some very heavy conversations about "making schools safe" in the province.I had just arrived in Toronto from Vancouver and had not really paid much attention to Ontario's Safe Schools Act, which seemed to be the focus of a lot of the conversations.The Safe Schools Act, passed in 2000, was really a bill amending On tario's Education Act, but it was always referred to as a discrete piece of legislation.The first thing I looked for was a definition of safety in the act.The first thing I noticed was that there wasn't one.So that was my point of entry.What did safety mean?Who got to define safety?How had the legislature and the public talked about safety before the bill's passage?And although school safety is an issue for all students, I was interested specifically in LGBTQ students.What did safety mean to LGBTQ students?How did they define safety, and how did that compare with what their schools were doing?Reading through newspaper and media accounts, I saw that the Safe Schools Act was in many ways a product of the zero-tolerance discussions xii
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.651 | 0.509 |
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".