Measuring gender in elementary school-aged children in the United States: Promising practices and barriers to moving beyond the binary.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
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.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it