Measuring Gender Identity and Religious Identity with Adapted Versions of the Multigroup Ethnic Identity Measure—Revised
Bibliographic record
Abstract
Adolescent identity develops across various domains (e.g., ethnicity, gender, religion). Although these domainsshare elements of identity (e.g., belongingness, self-categorization) there is a lack of continuity in the elementsselected when measuring various domains of adolescent identity. This study tested whether an adapted version ofPhinney and Ong’s (2007) Multigroup Ethnic Identity Measure—Revised (developed to measure adolescents’ethnic identity) could also measure gender and religious identities. Participants (N = 247, 56% female, Mage =13.33 years) completed adapted versions of the MEIM—R measuring gender identity (which we call theMulti-Identity Measure for Gender) and religious identity (Multi-Identity Measure for Religion). Confirmatoryfactor analysis models for the MIM-Gender and MIM-Religion scales demonstrated modest to good fits. Inaddition, the MIM-Gender and MIM-Religion scales demonstrated preliminary validity. These preliminaryresults suggest the adapted MEIM—R scales have the potential to measure domains of adolescent identitybeyond ethnicity.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".