Latino? Latinx? Latine? A Call for Inclusive Categories in Epidemiologic Research
Bibliographic record
Abstract
Use of the word "Latinx" has risen in popularity among both academics and nonacademics to promote a gender-inclusive alternative to otherwise linguistically gendered terms of "Latino/a." While critics claim the term is inappropriate for populations without gender-diverse individuals, or those of unknown demographic composition, increasing usage and among younger communities signals an important shift in centering the intersectional experiences of transgender and gender-diverse people. Amid these shifts, what are the implications for epidemiologic methods? We provide some brief historical context for the origin of the word "Latinx" along with its alternative "Latine" and discuss the potential consequences of its use for participant recruitment and study validity. Additionally, we provide suggestions for the best use of "Latino" compared with "Latinx/e" in several contextual circumstances. We recommend using "Latinx" or "Latine" in large populations, even without detailed data on gender, since there is likely gender diversity in the population, albeit unmeasured. In participant-facing recruitment or study documents, additional context is needed to determine which identifier is most appropriate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.296 | 0.298 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.011 | 0.069 |
| Scholarly communication | 0.017 | 0.037 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".