A mentoring community in STEM: Fostering STEM identity within universities
Bibliographic record
Abstract
Mentorship in science, technology, engineering and mathematics (STEM) disciplines has been shown to improve student persistence, academic performance, and STEM identity (Damkaci et al., 2017; Hernandez et al., 2017), especially for underrepresented groups (Chelberg & Bosman, 2019; Estrada et al., 2018). The current study used a mixed methods approach of surveys (n=182) and interviews (n=30) to explore the lived experience and perspectives of undergraduate and graduate students, staff, and scholars, in three STEM faculties in Canada. Findings related to connections between the mentorship experiences, STEM identity, belonging, community, and career aspirations of these individuals will be explored in this session. This research was approved by our institutional research ethics board.\nGiven the research on the value of mentorship, it was surprising that only one quarter of all participants indicated having a current mentor in STEM. For those participants without a current mentor, 75% expressed interest in having a mentor. Mentoring experiences were found to be more common for white participants than racialized participants, with undergraduate students indicating a desire for more mentoring opportunities and community building in their academic careers.\nNew mentoring initiatives in STEM will also be highlighted, with ensuing engagement in discussion of mentoring barriers and possibilities in participants’ own regions of influence in STEM.\nChelberg, K. L., & Bosman, L. B. (2019). The Role of Faculty Mentoring in Improving Retention and Completion Rates for Historically Underrepresented STEM Students. International Journal of Higher Education, 8(2), 39-48.\nDamkaci, F., Braun, T. F., & Gublo, K. (2017). Peer Mentor Program for the General Chemistry Laboratory Designed to Improve Undergraduate STEM Retention. Journal of Chemical Education, 94(12), 1873–1880.\nEstrada M., Hernandez P.R., & Schultz P.W. (2018). A Longitudinal Study of How Quality Mentorship and Research Experience Integrate Underrepresented Minorities into STEM Careers. CBE Life Sci Education, 17(1):ar9.\nHernandez P.R., Bloodhart B., Barnes R.T., Adams A.S., Clinton S.M., Pollack I., et al. (2017). Promoting professional identity, motivation, and persistence: Benefits of an informal mentoring program for female undergraduate students. PLoS ONE 12(11): e0187531.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.001 | 0.002 |
| 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".