Gender-identity typologies are related to gender-typing, friendships, and social-emotional adjustment in Dutch emerging adults
Bibliographic record
Abstract
The current study examined emerging adults’ gender identity and its link with several gender-related and social outcomes, by using a novel dual-identity approach that was originally developed in children. Dutch emerging adults between 18 and 25 years old ( N = 318, M age = 21.73, SD = 2.02; 51% female) indicated their similarity to the own-gender group and the other-gender group to assess gender identity. They completed questionnaires assessing gender-typed behavior (internalized sexualization, toughness, emotional stoicism) and attitudes (i.e., sexism); friendship efficacy and ability; and social-emotional adjustment. Cluster analysis on the gender-identity items revealed four gender-identity types: (a) feeling similar to one’s own gender, but not to the other gender (Own-GS); (b) feeling similar to both one’s own and the other gender (Both-GS); (c) feeling dissimilar to one’s own gender (Low-Own-GS); and (d) feeling similar to neither gender (Low-GS). Own-GS and Low-GS adults were most gender-typed in their behavior and showed sexist attitudes. Both-GS adults felt efficacious and were highly able to relate to both genders, whereas the other groups felt efficacious and were able to relate to only one gender (Own-GS, Low-Own-GS), or to neither gender (Low-GS). Low-Own-GS and Low-GS were least well-adjusted social-emotionally. Findings suggest that identifying with one’s own gender is helpful for certain aspects of social-emotional adjustment but that also identifying with the other gender provides the advantage of flexible social and interpersonal skills and egalitarian gender attitudes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".