Bibliographic record
Abstract
A basic premise of public scholarship is making academic work and related ideas accessible and available to publics. Media engagement, whether interviews with news journalists, or the use of hashtags, is a necessary feature of any public scholarship. Media formats play a fundamental and interactive role in how people ultimately come to view and understand the social world, having had a discernable influence on election outcomes, responses to global pandemics, and so on. The question is not whether scholars should engage with media but how to do so. Drawing on fifteen years of experience that includes hundreds of print, radio, and television news interviews, dozens of published opinion pieces, and the use of social media for public engagement, this book outlines a practical, easy-to-follow approach to doing public sociology in media that consists of, and brings together, interrelated forms of media engagement. This book also offers some advice pertaining to career advancement and provides strategies to avoid negative experiences. Doing Public Scholarship will be of general interest to those wanting to go public with their research.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.056 | 0.031 |
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".