Bibliographic record
Abstract
The goal of this investigation was to understand organizations’ responses to Black Lives Matter, a social movement that gained momentum in response to the deaths of George Floyd and other Black American citizens in 2020. Using racialized decoupling from Victor Ray’s racialized organizational theory, we resolved to investigate the congruency of companies’ existing diversity and inclusion policies and statements before the mainstream BLM movement. To investigate review corporate statements, proposed actions, and corporate social media posts during the mainstream BLM movement of 2020. In doing so we assessed if organizations have engaged in tokenism or made true commitments to reduce internal racialized structures. In analyzing the three sets of publicly available information of the top 50 companies from the July 2020–Fortune 500 list we employ a content analysis methodology and contribute to the literature by identifying and developing a 4-category classification by which to distinguish organizations’ varying commitments to reducing racial inequality. Our use of racialized organizational theory demonstrates an application of the theory to the BLM movement and provides argument for normative isomorphism’s influence on diversity programming.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".