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Record W4400279613 · doi:10.3138/jsp-2023-0081

Unraveling the Attributes of Productive Scholars in Social Science Fields: A Study of Chinese Scholars Publishing in Top-Tier International Journals

2024· article· en· W4400279613 on OpenAlexvenueno aff
Xiaohua Jiang, Ziqian Zhang

Bibliographic record

VenueJournal of Scholarly Publishing · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsPublishingSociologySocial sciencePolitical scienceLaw

Abstract

fetched live from OpenAlex

Aspiring researchers worldwide strive to amass a prolific publication record to elevate their academic standing and advance their research careers. However, achieving research productivity is a multifaceted endeavour shaped by individual characteristics, institutional support, and societal factors. This study explores the experiences of ten high-performing Chinese scholars in social science fields. By examining their prolific publication records in top-tier international journals, the study unveils the distinct attributes that characterize productive researchers and the strategies they employ to facilitate their scholarly publishing. Through in-depth interviews and an exploration framed by academic identity and academic socialization, the study highlights the significance of cultivating a robust academic identity, fostering efficient scholarly practices, and promoting effective collaboration. These findings offer invaluable insights for emerging academics seeking to enhance their research productivity without compromising the quality of their work, especially concerning publications in esteemed international journals.

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)0.033
metaresearch head score (Gemma)0.032
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Research integrity
Consensus categoriesMetaresearch, Scholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0330.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0240.122
Open science0.0020.000
Research integrity0.0000.003
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 head0.044
GPT teacher head0.370
Teacher spread0.325 · 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 designObservational
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

Citations1
Published2024
Admission routes1
Has abstractyes

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