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Record W3176320011 · doi:10.3138/jsp.52.4.02

Career Difficulties That Chinese Academic Journal Editors Face and Their Causes

2021· article· en· W3176320011 on OpenAlexvenueno aff
Zhiwu Xu, Dandan Yang, Chen Bing

Bibliographic record

VenueJournal of Scholarly Publishing · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsSalaryPromotion (chess)PublishingFace (sociological concept)Quality (philosophy)ChinaWork (physics)Job satisfactionPsychologyPublic relationsMedical educationSociologyManagementPolitical scienceSocial scienceSocial psychologyMedicineEngineeringLawEconomicsPolitics

Abstract

fetched live from OpenAlex

The purpose of this article is twofold: 1) to analyse common career difficulties experienced by academic journal editors in China and explain their causes; and 2) to identify how stakeholders in Chinese scholarly publishing can support editors. Thirty-two academic journal editors were surveyed, and fourteen of those were subsequently interviewed. We found that a deficit of high-quality manuscripts, a large number of laborious tasks at work, limited opportunities for professional advancement, and low job satisfaction were the main career difficulties, of which the two most common were a deficit of high-quality manuscripts and low job satisfaction. The key causes of these difficulties were an unbalanced academic evaluation system that rewarded indexed over non-indexed journals and the marginal status of journal offices at their affiliated institutions. The forms of support most desired by respondents were recognition for their work, salary increases, greater opportunities for continued learning, easier job title promotion, and more scholarly communication with their peers.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0440.447
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.287
Teacher spread0.248 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
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

Citations2
Published2021
Admission routes1
Has abstractyes

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