Measuring gender in elementary school-aged children in the United States: Promising practices and barriers to moving beyond the binary.
Bibliographic record
Abstract
How gender identity is assessed directly shapes how students are supported in elementary schools in the United States. Despite the existence of gender diversity, calls for more inclusive science, and recommendations from national research associations and societies to incorporate and emphasize the voices of individuals with diverse gender identities, most studies exploring gender disparities in education have relied heavily on the assumption of a gender binary. As a result, the omission of diverse gender identities from educational research in the elementary years is troubling. To address this area of need, the current article summarizes the opportunities for and constraints surrounding inclusive evaluation of gender identity in the elementary school years. We begin with a brief review of common methods used to assess gender identities for children in elementary school, including the strengths and limitations of each. We next contextualize these measures by outlining the current state-level barriers to including diverse gender identities in assessments of gender. In highlighting the best available practices and the structural systems of oppression realized through state-level policies that perpetuate an inability to represent student voices across the gender spectrum, we conclude with a call to action to inspire the evolution of best practices in the service of all students. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.023 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".