Understanding Networks of Support for Underrepresented Students in STEM
Bibliographic record
Abstract
The success and persistence of women and other under-represented minorities in science, technology, engineering and mathematics (STEM) post-secondary education has been studied from numerous perspectives. Recently, researchers have suggested that success and persistence in STEM fields depends on the development of a strong STEM-identity . However, to develop a complex picture of how students access resources to develop STEM identities it is necessary to understand the formal and informal structures and relationships at the university and beyond which support them and enable them to support others. We know that under-represented students tend to persist in STEM if they become involved in initiatives such as equity, diversity and inclusion in STEM campus groups. Despite evidence that participation in these initiatives can contribute to persistence, we know relatively little about what how students access and participate in these initiatives, or how these spaces can facilitate undergraduate students’ identity work in STEM. This presentation explores the usefulness of social network analysis to understand the degree to which these initiatives may create networks of support for students’ persistence in STEM. We also suggest the need for qualitative analyses to further understand and characterize the resources these networks provide.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".