From Access to Success: Identity Contingencies & African-American Pathways to Science
Bibliographic record
Abstract
We conducted a mixed-methodological study of matriculation issues for African-American students in science. The project compares the experiences of students currently majoring in science (N= 304) with the experiences of those who have succeeded in earning science degrees (N=307). Using a 57-item Likert scale questionnaire, participants were asked about their experiences based on theories that are commonly used to explain matriculation issues (Stereotype Threat, Microaggressions, Communities of Practice). The results of the study revealed that although both groups recognized the major role of race in their experiences, the primary factor distinguishing between groups was a sense of alignment with the community (Communities of Practice) and their differences with experiencing Microaggressions. Those who achieved success were far more likely to report a weak sense of belonging and were far more likely to report experiences with Microaggressions. By contrast, students were more likely to feel comfortable with the science community and less likely to report experiences with Microaggressions. The findings of this study are indicative of the pervasive impact of racial bias and conflict as a gatekeeper in providing access to science careers.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".