Accessibility, Diversity, and Inclusion: Recommendations to Increase Diversity and Inclusion on the City of Guelph's Accessibility Advisory Committee
Bibliographic record
Abstract
This report identifies practical strategies for improving diversity and inclusion on the City of Guelph’s Accessibility Advisory Committee (AAC). It is hoped that this report will support recommendations brought to the City of Guelph’s Council, prompting policy changes to improve the engagement, participation, and inclusion of diverse groups within the city’s Advisory Committees of Council, specifically the AAC. \n \nFindings suggest that strategies to improve diversity and inclusion on the AAC fall into four categories: (1) Fostering Inclusive and Culturally Sensitive Environments; (2) Facilitating Diverse and Inclusive Recruitment; (3) Inclusive Onboarding and Membership Supports; and (4) Data Collection.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.048 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.235 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".