Understanding Australian Academic Authors in the Humanities and Social Sciences Their Publishing Experiences, Values, and Perspectives
Bibliographic record
Abstract
Publishers of academic books in Australia have evolved in response to the crisis in scholarly publishing by adapting to the opportunities afforded by digital technologies for faster, cheaper, and more dynamic publishing approaches. Academic authors are at the core of the scholarly publishing landscape, so publishers need to understand their motives and needs. This paper examines data from a survey of academic authors in the humanities and social sciences (HSS) in Australia. Our aim for the survey was to understand the publishing experiences, behaviours, and perceptions of these authors. We discovered their expectations for publishers are high. They want fast turnaround, high-quality editing and production values, and cheaper books, which run up against three principal constraints for all scholarly publishers: quality, time, and cost. The prestige and reputation of a publisher are critical, and authors are primarily interested in traditional success measures of academic performance. Societal impact or engagement with research end-users was seen as less important. The findings of this project highlight a number of contradictions and tensions within the scholarly publishing landscape, and they have tangible implications for practices in HSS for authors and publishers, as well as for grant funders and university administrators who adopt policies and assign criteria for research evaluation.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".