Bibliographic record
Abstract
For years, diversity scholars have been calling for more empirical studies that specifically show how linguistic and non-linguistic practices produce asymmetrical differences between and among social groups. To that end, we show that textual analysis methodologies can provide situational, contextual, and empirical research that demonstrates practices and productions of these differences in organizations and workplaces. We further provide researchers with two overlooked approaches of textual analysis methodology that add a multi-level organizational dimension to studying the production of these differences—critical sensemaking and discourse theory. By establishing and maintaining contextual relevance and casting organization as socially constructed on multiple levels, these two approaches help point to systemic-wide strategies for addressing critical organizational, institutional and societal diversity issues such as discrimination or harassment. This chapter will be useful for the diversity researcher who studies linguistic and non-linguistic practices in organizational, institutional, and social formations.
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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 | 0.029 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.016 | 0.033 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.017 | 0.019 |
| Insufficient payload (model declined to judge) | 0.102 | 0.025 |
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".