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Record W3120462291 · doi:10.3138/jsp.52.2.01

Book Publishing in the Humanities and Social Sciences in Australia, Part One: Understanding Institutional Pressures and the Funding Context

2021· article· en· W3120462291 on OpenAlexvenueno aff
Agata Mrva-Montoya, Edward Luca

Bibliographic record

VenueJournal of Scholarly Publishing · 2021
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsnot available
Fundersnot available
KeywordsPublishingPublicationAutonomyContext (archaeology)Political scienceFace (sociological concept)Public relationsLibrary scienceSociologySocial scienceMedia studiesLawGeography

Abstract

fetched live from OpenAlex

This is part one of a two-part study on the publishing behaviours of academics in the humanities and social sciences (HSS) at Australian universities. Our data consist of semi-structured interviews with twenty-one participants. Part one explores how current institutional pressures and the research funding environment are shaping academics’ book publishing practices. In particular, we attend to the growing concerns of academics relating to the measurement and ranking of universities, which are driving performance expectations for publishing, and we examine how this trend is influenced by changes in governmental policy and the requirements of funding bodies. We found that Australian HSS academics face increasing pressure to publish journal articles rather than books, to publish books with prestigious international publishers, and to secure external funding for their research. These pressures could restrict their scholarly autonomy or even influence their research agenda. We contend that these developments have concerning implications for HSS in Australia.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.008
Science and technology studies0.0040.004
Scholarly communication0.0070.005
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.804
GPT teacher head0.515
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainIncentives
GenreEmpirical

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".

Quick stats

Citations14
Published2021
Admission routes1
Has abstractyes

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