Centering the Margins: Methodological Challenges and Opportunities of Studying the Understudied
Bibliographic record
Abstract
Diversity, equity, and inclusion (DEI) is a critical research area in management that often entails examining populations that have historically been understudied. Past scholarship has highlighted how studying such populations can be connected with various methodological challenges and complexities. For instance, understudied populations often present difficulties with achieving a large enough sample size. They may also be more challenging to gain access to. However, researchers have also identified ways to attend to, and potentially address, some of these challenges and complexities. Accordingly, we have three goals in this panel. First, we aim to highlight opportunities for researching understudied populations in DEI scholarship. Second, we aim to discuss the methodological challenges associated with such research. Third, we aim to offer researchers strategies for navigating and addressing these challenges. To support these three goals, we have compiled a panel of expert DEI researchers, who will draw on their past experiences to address these three aims and, ultimately, forward audience members’ understandings of these topics.
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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 | 0.390 | 0.531 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.016 | 0.036 |
| Scholarly communication | 0.029 | 0.030 |
| Open science | 0.008 | 0.035 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".