Bibliographic record
Abstract
In Canada and across the globe, transgender people continue to be marginalized, simply for living their truth. Members of this community are often victims of discrimination and structural-racism in respect to; housing, employment, and government assistance. In Canada, only five provinces have implemented laws aimed to protect citizens on the basis of gender identity and gender expression. Due to the fact that the majority of Canadian provinces and territories do not have laws which protect Trans people; many employers, landlords, and government officials are legally able to discriminate against them. They are deprived of employment, shelter and support. In addition to discrimination, these individuals are also at a greater risk for violence and harassment. Further, trans women of color prove to be the most vulnerable within the transgender community leading with the highest homicide rates in the world (Human Rights Campaign). As a result of the rising rates of violence targeting this community, many activists have called for a trans state of emergency. It is evident due to the ill found nature of Canada’s legislative system, the Canadian Human Rights Act and the Criminal Code need to be amended in order to provide equal protection under the law, and address the current issues that are having disastrous effects on transgender community in Canada.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.158 | 0.034 |
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".