Best Practice or Buzzword? the Opportunities and Challenges of Mentorship for EDI in Creative Technology
Bibliographic record
Abstract
ABSTRACT This paper explores mentorship as a much‐celebrated strategy for improving equity, diversity, and inclusion (EDI) across a range of exclusionary working sectors. As a tactic for addressing underrepresentation and scaffolding entry, progression, and success within historically homogenous industries, mentorship is seen as a normatively beneficial practice. Yet, despite its association with greater opportunities and potential for breaking barriers, mentorship is rarely defined and how it is enacted is typically absent from discussion. Our research project tackles this ambiguity on the impact of mentorship for EDI aims and values, with a specific focus on creative and technological industries where exclusions in participation remain pernicious. Drawing on critical feminist analysis of public‐facing materials about mentorship in these sectors and 40 interviews with mentorship program organizers and creative tech workers who have engaged in mentorship relationships, we outline the characteristics of mentorship activities from the perspective of three key stakeholders in EDI‐ corporate units such as employee resource groups, third‐party companies who provide mentorship services to organizations and individuals, and community groups featuring mentorship as part of their activities. Our exploration of these three distinct models of mentorship demonstrates that the context where these activities are organized shapes their implementation, evaluation, and overall potential impacts, including for intersectional feminist aims. We conclude by arguing for the value of communal‐based approaches to mentorship for more transformative outcomes related to equity, diversity, and inclusion.
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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 | 0.083 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.050 |
| Scholarly communication | 0.030 | 0.026 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".