Diversity, Equity and Inclusion in the Implementation of Indigenous Relations and Leadership Competencies in Leadership Competitions at the BC Office of the Auditor General
Bibliographic record
Abstract
The BC Public Service is working towards improving diversity, equity, and inclusion (DEI). The BC Office of the Auditor General (BC OAG) is also making commitments to improve DEI in the organization. The purpose of this thesis is to assist with these improvements, focusing on how DEI can be better incorporated into hiring practices for leaders at the BC OAG. Specifically, this thesis is seeking to determine how the BC OAG implements competencies in leadership competitions in a way that aligns with these DEI commitments. To assess this, the researcher undertook a qualitative mixed methods research approach, consisting of a cross-jurisdictional scan of Canadian audit offices, structured interviews with BC OAG staff members who had been panelists on leadership competitions, and a document review of leadership competition files. From the cross-jurisdictional scan, the key finding is that Canadian audit offices value and plan around DEI quite differently from one another. The key finding from the structured interviews is that DEI is not a requirement in competency implementation at the BC OAG, nor is it a requirement for panelists to utilize a DEI lens in their role on leadership panels. The key finding from the document review is that the competencies the BC OAG utilizes in leadership competitions have the potential to incorporate DEI, but this incorporation is inconsistent. From these findings, an option was presented to the BC OAG to develop its own explicit DEI competency that is tested for in every leadership competition.
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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 | 0.028 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.014 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".