Public Geographies and the Gendered Experience of Saying “Yes” to the Media
Bibliographic record
Abstract
Debates about public scholarship and research impact are vital for geographers, helping us examine who our work is for and how we can make change in the world. Although there is a vibrant literature on geography’s “public voice,” we have not significantly engaged with who contributes to public scholarship—and participation is neither equal nor equally valued. Using a case study of academic women’s contributions to the media, this article examines how gender interacts with academics’ motivations and concerns about public scholarship. Beyond the question of whether the free labor of media engagement is worth one’s time among the various pressures of the neoliberal university, I argue that we do not come into or contribute to public scholarship equally. The article centers three interconnected factors that currently limit equitable participation: gendered social norms about who is an “expert” (confidence and everyday sexism), job security (pretenure and never-tenured precarity), and a fear of backlash (from online insults to death threats). I conclude by offering practical suggestions for supporting diverse public scholarship, while also recognizing the many “limits to dialogue.”
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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.020 | 0.059 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".