Organisational experimentation in favour of ethnocultural diversity:An action-research study in a Canadian union
Bibliographic record
Abstract
This paper examines a study conducted within a larger action-research project designed to support a union’s initiative to rethink its capacity to ensure the building of an ethnoculturally diversified pool of internal staff. In this paper, we present our action-research framework as well as focus on one of the studies conducted between 2022 and 2024 in a collaborative approach to examine the level of ethnocultural diversity among the union’s professional staff In this first micro-study, we gathered a first set of data regarding their experiences in relation to the matter through a survey (n= 304 responses) distributed among the union’s professional staff. Results provide various insights both by confirming homogeneity among staff and fleshing out specific educational and professional paths undertaken by staff members identifying with ethnic minorities. More open questions in the survey also provide staff’s testimonies of cases of discrimination within the organisation’s staffing and performance appraisal practices. The union has approached this reflexive exercise as an organisational experimentation, which will involve a long process with various stages of investigation, reflection and action.
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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 | 0.023 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.057 | 0.026 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| 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".