Reducing Intellectual Poverty of Outsiders within Academic Spaces through Informal Peer Mentorship
Bibliographic record
Abstract
Academia is changing and a growing number of academics are finding themselves in non-tenure-track positions, experiencing increasing numbers of career transitions, or following alternative career trajectories. Academics in these positions often find themselves positioned as outsiders within their institutions and/or the broader academic community. In this article, the authors draw on narratives from eight members of an international peer mentoring group to examine the nature of being an outsider within academia, and the role that informal peer mentoring can play in reducing intellectual poverty for academics in outsider spaces. The findings illuminate the nature of intellectual poverty they experienced—including isolation and invisibility—lack of access to institutional knowledge, and lack of resources for professional development. The participants’ narratives also highlight the ways in which peer mentoring enhanced their professional development, allowed participants to access support beyond institutional and geographic boundaries, and provided social support and motivation to move forward in scholarly pursuits.
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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.029 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".