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Record W3164514146

Publishing trends in the International Journal of Sport Psychology during the First 50 years (1970-2019), with a particular focus on Asia and Oceania

2021· article· en· W3164514146 on OpenAlexaboutno aff
Peter C. Terry, Renée L. Parsons-Smith, Alessandro Quartiroli, Susan M. Blackmore

Bibliographic record

VenueUniversity of Southern Queensland ePrints (University of Southern Queensland) · 2021
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsnot available
Fundersnot available
KeywordsPublishingLibrary sciencePolitical scienceSocial scienceGeographyHistorySociologyLaw
DOInot available

Abstract

fetched live from OpenAlex

To commemorate the 50th anniversary of its first issue, we explored publication trends in the International Journal of Sport Psychology (IJSP), with a particular focus on research contributions from Asia and Oceania. A descriptive analysis of all articles published in IJSP between 1970 and 2019 (N = 1,175) was conducted to identify trends related to first author gender, country, and continent. Also, an analysis of research topics by decade was conducted using Leximancer. Key findings were: (a) female first authors became more prominent over time but remained in the minority; (b) the percentage of articles from Europe and Asia increased and the percentage of articles from North America declined, although the USA and Canada have been the top contributors over the life of the journal; and (c) the focus on particular topics, especially those pertaining to athletes, performance, motor learning, motivation, and teams was sustained throughout the 50-year period. Within Asia and Oceania, the 10 countries publishing the most articles were, in descending order, Australia, Israel, Hong Kong, Taiwan, South Korea, New Zealand, India, Japan, Singapore, and Turkey.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.008
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
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 head0.091
GPT teacher head0.337
Teacher spread0.246 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2021
Admission routes1
Has abstractyes

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