Incite then Control: Organizational Responses to Managers’ Appropriations of Diversity Practices (WITHDRAWN)
Bibliographic record
Abstract
The diversity management literature offers few accounts of organizations that manage to foster diversity, of how diversity policy and practices are enacted in practice, and of managers’ motivations to do so. To close these gaps, this paper reports a 4-year ethnography in a European bank which succeeded in reducing the glass ceiling by inciting managers to appropriate its gender diversity policy and practices. I examine the multiple types of appropriation (e.g., selective appropriation, extension, diversion, misappropriation) as well as managers’ underlying motivations for each of them. I then analyze the continuum of organizational responses to these appropriations, from undoing to appropriation of managers’ appropriations. This paper contributes to a) providing a better conceptualization of appropriation by extending previous typologies and highlighting the organizational repertoire of action to react to managers’ appropriations; b) shedding a new light on the so called ‘resistance to change’; and c) providing insights into the advantages and drawbacks of appropriation as a new model of practice implementation. It thus contributes to advancing and renewing both the diversity and change management literatures.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".