Call for the Federal Public Service to create an initiative to recruit and hire employees with Autism:
Bibliographic record
Abstract
Individuals with autism, a neurological condition impacting everyday activities, make up about 1.5-2.5% of the population. Compared to people with other disabilities, those with autism are disproportionately unemployed and underemployed. The Canadian federal government’s National Autism Strategy, consisting of research and funding to improve the health and well-being of those with autism, is led by the Public Health Authority of Canada (PHAC), but Employment and Social Development Canada (ESDC) plays an increasingly important role in supporting those on the spectrum through its programs and responsibility for implementing the Accessible Canada Act. ESDC, along with the Canada Revenue Agency (CRA) are two of the largest federal public sector employers. The writer, an autistic individual themself, advocates for ESDC and CRA to take the lead in creating a specialized hiring and retention process adapted to autistic individuals, as hiring and retaining autistic staff will likely produce a win-win result, helping the organizations become more diverse, higher performing and agile while reducing turnover and therefore staffing and training related costs.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.021 | 0.012 |
| Insufficient payload (model declined to judge) | 0.136 | 0.045 |
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".