Bibliographic record
Abstract
Despite continued appeals by funding bodies, universities, and academy-based professional organizations to engage in knowledge mobilization, few academic researchers have made convincing and sustained efforts to dismantle the existing dominant power architecture that orders and organizes professional merit hierarchies along the lines of publication prestige (as indicated by the reputation of publishers) rather than on the basis of readership size or publication impact. The authors encourage more academics to write for a broader public audience. After highlighting a few common reasons why so much academic writing fails to engage readers beyond specialist audiences, the authors turn to the stories of five academic writers whose books have reached hundreds of thousands of people. These five books were selected because they were published within the last 10 years, were widely read, and were based in a qualitative, ethnographic research approach. Because they wished to reflect on the unique conditions shaping work within institutions of higher education, the authors excluded journalists and professional writers and included only university faculty. The authors interviewed these five authors, asking them about their writing styles, their publication-related experiences, and the production and distribution processes of their work.
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.024 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.009 |
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".