Navigating the Path to Diversity, Equity, and Inclusion in Strategy Making
Bibliographic record
Abstract
Creating strategies has traditionally been a reserved and secretive endeavor, limited to a select few who possess the authority to make decisions. Unfortunately, these traditional strategists often lack diversity. In response to pressing societal issues and growing environmental complexities, organizations are now embracing collaboration and diversifying the actors involved in strategic management. Despite these positive shifts, the expectations for representation and equal rights for marginalized social groups in strategic management are not fully realized, maintaining persistent pressure for change. Current research on diversity in strategy making, whether concerning the diversity of top management teams or adopting open strategy approaches, primarily takes a utilitarian perspective on diversity and inclusion. This symposium aims to stimulate a critical dialogue on diversity, equity, and inclusion in strategy-making. By exploring various perspectives and research streams within the domain of diversity, equity, and inclusion, the symposium seeks to shed light on different methods to enhance diversity, equity, and inclusion in strategy making. The discussions will delve into understanding the inherent challenges and identifying effective strategies to overcome them within the context of strategy-making.
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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 | 0.042 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.017 | 0.105 |
| Scholarly communication | 0.032 | 0.025 |
| Open science | 0.002 | 0.031 |
| Research integrity | 0.004 | 0.008 |
| 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".