Paired Measures of Competence and Confidence Illuminate Impacts of Privilege on College Students
Bibliographic record
Abstract
We seek to understand how the experiences of groups that differ in gender, ethnicity, and sexual orientation produce college-level educational performances that differ from the experiences of the dominant majority group. We employ two datasets: a National Database of 24,701 participants and a Paired-Measures Database with 3,323 participants. Both datasets provide demographic information, socioeconomic conditions of status as first-generation student, English as a first language, and interest in majoring in science, and competency scores on understanding science as a way of knowing obtained from the Science Literacy Concept Inventory. The Paired-Measures Database includes additional self-assessed competence ratings that enabled quantifying affective confidence. We meld the ways of knowing of ethics, numeracy, and social justice, especially the social justice concept of Othering, to interpret our data. Two of three competing hypotheses about self-assessment encourage Othering. Our data strongly support the third—that all groups are good at self-assessment and merit equal respect. Women and men are equally competent in science literacy. Women, on average, are more accurate in their self-assessments whereas men, on average, are overconfident. Those with minority sexual orientations register higher competence than the binary-sexual majority but are less confident of their competency. Minority ethnicities, on average, produce significantly lower science literacy scores. With one exception (Middle Eastern), groups produce mean self-assessed competence ratings that are remarkably accurate predictors of their mean competence scores. The three socioeconomic conditions exert significant and unequal impacts across ethnic groups, with Hispanic, Middle Eastern and Pacific Islander data providing some unique results.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".