Getting Good Data to Evaluate Employment Equity Initiatives: An Example from Canada
Bibliographic record
Abstract
This chapter provides a clear understanding of relationships as described by John Mayne. Getting good data and putting them together remains an ongoing challenge for evaluators providing information to decision makers. The chapter presents the challenges facedbythe Public Service Commission of Canada in getting accurate and timely results for one of their program responsibilities and the consequences for program decision making. The methodology was a non-experimental design using periodic data over a long period of time. Success was determined by evaluating improvements over time. The specific program is in the area of employment equity—Canadian programming in the tradition of achieving greater social equality in Western democracies. The case illustrates the care that needs to be taken in interpreting results. While particular measures appeared credible, were repeatedly used, and confirmed existing beliefs, a closer examination of the underlying methodology and restrictions on some of the measures placed significant limitation on the data that were not initially recognized.
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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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.014 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".