Strategies for Mentoring and Advising Evaluation Graduate Students of Colour
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract: While evaluators have many intersecting identities, ethnicity remains the most salient identity for evaluators of colour. As formal graduate training in evaluation continues to expand, so too does the number of students from ethnic minoritized populations, who are in need of specialized mentoring and advising. Drawing from previous research on evaluation, higher education literature, and personal reflections from the author, an Afro-Latina faculty member, this practice note outlines five strategies for mentoring and advising evaluation graduate students of colour. These include considering the impact of vicarious trauma; assisting with the facilitation of peer and mentor “squads”; respecting, honouring, and celebrating students’ culture, religion, and families; being vigilant of microaggressions and practicing microvalidations; and developing mentoring competence. Each strategy is presented along with reflections and practical examples for implementation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it