Towards More Equitable Public Sector Service Delivery: Using Critical Theories to Cultivate the Depth of the Equity Analysis Tool Facilitation
Bibliographic record
Abstract
This paper provides a reflective account of the insights gained in the design of instructional resources intended to enhance attendee engagement of the Equity Analysis Tool (EAT) facilitation sessions. The EAT, implemented by the municipal Equity Team, serves as a framework for City staff to identify gaps in their respective work areas, thereby advancing a more equitable public sector service delivery. Applying a framework grounded in critical theories, I draw attention to how the learning experiences associated with the EAT facilitation are connected to broader social contexts as well as the diverse identities of the attendees themselves. I then discuss how I customized content to optimize learning and practical application of EAT and how I connected critical reflection to critical action to cultivate the depth of the EAT facilitation. I conclude by highlighting the significance of building trust among facilitators and attendees in a supportive environment conducive to transformative learning experiences.
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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 | 0.054 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.012 | 0.035 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".