Managing Workforce Diversity in Canada: An Empirical Study of the Factors Affecting the Adoption and Success of Diversity Strategies in Canadian Organisations
Bibliographic record
Abstract
Equality, diversity and inclusion (EDI) in the workplace continues to be a \ndominant universal issue. Through its Employment Equity Act (EEA), Canada has \nacted as an exemplar in influencing equality legislation in other countries. The \nCanadian government’s thirtieth EEA annual report to Parliament presents a very \npositive picture of equality in employment for the four designated groups, DG: \n(women, Aboriginal peoples, persons with disabilities, and visible minorities) in \nthe four industry sectors (Banking, Communication, Transportation, Other) \nfederally regulated under the EEA’s legislated employment equity programme \n(LEEP). However, this claim of success is challenged in this study as specious in \nshowing uniform take-up using aggregated LEEP data. A theoretical model is \ndeveloped between variables representing external pressures, at the macro national level, internal pressures, at the meso-organisational level, and their \nhypothesized relationships with reactive and proactive EDI focused programmes \npursued by LEEP organisations. This model is empirically validated by applying \npartial least squares structural equation path modelling to data collected from 440 \nLEEP organisations. Findings reveal that all four DGs are substantially under represented, relative to their labour market availability (LMA), in the majority of \nindividual LEEP organisations, despite over three decades following EEA \nimplementation. DG-LMA representation was also found to differ by industry \nsector. The main contribution to knowledge of this study is the introduction of a \nvalidated predictive EDI model developed and empirically validated for the four \ndesignated groups in the context of Canada. Applications of this generic model \nto other countries for benchmarking and comparative studies could contribute to \nEDI theory, practice and policy, internationally.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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; a candidate call from one teacher head, 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".