Elements for a supportive and inclusive employment program for trans individuals in Vancouver (located on the traditional territories of the Coast Salish peoples)
Bibliographic record
Abstract
Di ability Management Term• 1npl ym nt r gram: r gram d 1gn d t a t p p i ith obtaining empl ym nt thr ugh upp rt and kill d nt.• R turn-to-w rk (RTW) pr gram: Thi t m1 can b u d int r hang ably with mp l yment pr gram.It r D r t a p cifi pr gram d igned within th w rkpla for an empl y wh ha b en on ick I a e nd i r tun1ing t w rk. Tran pecific TerminologyQu n ni er it ' Po iti e pace (n.cl .)gr up pr id d th D 11 wing d finitions ofk y tenn :• Tran phobia: riminat ry treatm nt t ward pe ple wh d n t identify or pre nt with the conv ntiona1 con pti n of gend r binarie .(female and mal )• Gender identity: A per on' self-image about th ir gender.Thi can be different from a per on's a igned ex.A per on' gender identity i not a per on' sexual identity.• Two-spirit: Thi tenn i u ed by orth A1nerican Fir t Nation to refer to people who have both the male and the fe1nale pirit.The U Berkeley Gender Equity Re ource Center (n.d.) provided the following definition of key tenns:• Cisgender: Thi tenn is used to define a person who is either born into their preferred gender/sex or who chooses to confonn to conventional gender/sex exp ctation .• isgenderism: When a per on make the a umption that everyone is ci gender and in tum creates marginalization towards a p r n who identifie a oth r than ci gender, thi
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".