Language Matters: Exploring Preferred Terms for Diverse Populations
Bibliographic record
Abstract
This article explores the significance of employing preferred terms and inclusive language in research practices concerning diverse populations. It highlights how inappropriate terminology can lead to labeling, stereotyping, and stigma, particularly for equity-denied groups. The study aimed to identify and analyze terminology preferences for diverse communities by major international organizations. Through a systematic environmental scan methodology, data were collected from 12 prominent organizations. The results indicate a concerted effort toward adopting inclusive language, with organizations favoring respectful and accurate terminology. For instance, terms like "people made vulnerable by systemic inequities" and "migrant workers" were preferred over outdated or stigmatizing alternatives. The discussion emphasizes the importance of identifying conflicting terms and trends in terminology preferences over time. We recommend prioritizing the use of preferred terms to promote respectful and accurate discourse, with a focus on person-centered language. Ultimately, the findings underscore the critical role of language in shaping perceptions and attitudes toward diverse communities, and advocate for continued efforts to promote inclusivity and equity in research, policy, and practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".