Tapping into Talent: Neurodiversity Employment Research Project
Bibliographic record
Abstract
Over the past few years, there has been an emerging landscape of corporate neurodiversity employment programs developing across the globe. Funded by Canada’s Social Sciences and Humanities Research Council (SSHRC), this project explores and investigates the organizational processes, management principles, and work practices used by companies with established employment programs catered to neurodivergent individuals. As of 2021, the research team has studied more than a dozen global organizations, ranging from non-profits, social enterprises, and for-profit firms.\nThe Neurodiversity Employment Research Project is an initiative at the Ivey Business School led by Professor Robert Austin. The following video details an overview of the research project – its history, its purpose, and its findings so far. The initial analysis suggests that neurodiversity employment programs are a win-win for all those involved: the programs themselves grant career opportunities to neurodivergent individuals, while simultaneously enabling firms access to a diversified talent pool. The goal of this project is to contribute to an improved understanding of best practices for neurodiversity employment programs with the hope that these programs continue to expand in the years to come.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".