Testing intersectionality of race/ethnicity × gender in a social–cognitive career theory model with science identity.
Bibliographic record
Abstract
Using social-cognitive career theory, we identified the experiential sources of learning that contribute to research self-efficacy beliefs, outcome expectations, and science identity for culturally diverse undergraduate students in science, technology, engineering, and math (i.e., STEM) majors. We examined group differences by race/ethnicity and gender to investigate potential cultural variations in a model to explain students' research career intentions. Using a sample of 688 undergraduate students, we ran a series of path models testing the relationships between the experiential sources, research self-efficacy beliefs, outcome expectations, and science identity to research career intentions. Findings were largely consistent with our hypotheses in that research self-efficacy and outcome expectancies were directly and positively associated with research career intentions and the associations of the experiential sources to intentions were mediated via self-efficacy. Science identity contributed significant though modest variance to research career intentions indirectly via its positive association with outcome expectations. Science identity also partially mediated the efficacy-outcome expectancies path. The experiential sources of learning were associated in expected directions to research self-efficacy with 3 of the sources emerging as significantly correlated with science identity. An unexpected direct relationship from vicarious learning to intentions was observed. In testing for group differences by race/ethnicity and gender in subsamples of Black/African American and Latino/a students, we found that the hypothesized model incorporating science identity was supported, and most paths did not vary significantly across four Race/Ethnicity × Gender groups, except for 3 paths. Research and practice implications of the findings for supporting research career intentions of culturally diverse undergraduate students are discussed. (PsycINFO Database Record (c) 2019 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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".