A Reckoning: Exploring the History and Evolution of Diversity & Inclusion
Bibliographic record
Abstract
This major research project explores the history and evolution of diversity management strategies, like Diversity & Inclusion (D&I) and its current evolution, Diversity, Equity, and Inclusion (DEI). Born out of the American civil rights movement, the first phase of D&I emerged as a response to regulatory requirements for fair and equal opportunity in hiring practices. Since then, it has been widely embraced as a competitive advantage and the key ingredient to innovation. For half a century, organizations have been investing multi- billion dollars in D&I initiatives but still struggle to make meaningful and measurable progress towards racial equity. \nThis paper seeks to investigate how effective D&I strategies are and lessons learned from a half century of effort to diversify workforces and build inclusive workplaces. To answer this question, a literature review and jurisdiction scan on Canadian and American D&I efforts was completed to identify key trends and gaps, with a focus on the hiring and career advancement of Black employees. It also provides an overview of the legal, academic and corporate definition of D&I, in order to map out the initial and evolving goals of diversity, along with how it is implemented and evaluated to make the argument that D&I, when done right, is about culture change and organizational redesign.
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 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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.019 | 0.049 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".