What faculty say, and convey, matters: Interactions with underrepresented students in science, technology, engineering, and mathematics.
Bibliographic record
Abstract
Join us for this invited presentation as we examine students perceptions of their interactions with faulty. The participants in this study were primarily undergraduate students; however, approximately one-quarter of the participants were graduate students and post-doctoral research assistants. The entire sample consisted of students from a variety of postsecondary institutions including public and private, predominantly White, historically Black and Hispanic serving institutions from across the United States. This research is part of a larger study conducted over a 5-year period (2004–2009) with the National Society of Black Physicists (NSBP) and the National Society of Hispanic Physicists (NSHP). The majority are physics majors; however, some students are pursuing dual degrees in other STEM disciplines such as math, astronomy, and engineering. The findings of this study indicate that their interactions with faculty in the classroom and in advising session are critical. When those interactions are positive students benefit tremendously; however, in many instances they are negative and the interactions can cause barriers to their engagement in learning process and in how supported students feel pursuing science. Join us as we discuss some of the challenges and opportunities that these students encounter in their interactions with faculty.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".