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Record W2883553729 · doi:10.3138/jsp.49.4.05

A Mixed-Methods Study of the Ex Post Funding Incentive Policy for Scholarly Publications in Turkey

2018· article· en· W2883553729 on OpenAlexvenueno aff
Selçuk Beşir Demir

Bibliographic record

VenueJournal of Scholarly Publishing · 2018
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsnot available
Fundersnot available
KeywordsIncentiveTurkishProductivityQuality (philosophy)Political sciencePublic relationsBusinessAccountingEconomicsEconomic growth

Abstract

fetched live from OpenAlex

Some governments around the world support researchers with financial bonuses for scholarly publications to encourage their productivity. This convergent parallel mixed-methods study investigated whether the ex post funding policy in Turkey, instituted in late 2015, has influenced the quantity and quality of scholarly publications, and whether it has affected the quality of faculty instructional services as measured by student satisfaction. In addition, the study examined whether the financial support provided as a source of motivation has led to any ethical problems. The results indicate that while the ex post funding system has helped increase the number of articles published in journals indexed in national databases, it seems to have resulted in a decrease in those published in international journals indexed by the Web of Science. The results also indicate that the practice of ex post funding seems to have led to an ethical problem in the renaming of Turkish academic congresses. Finally, the policy appears to have negatively affected student satisfaction with the services offered to them by faculty at state universities.

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)0.150
metaresearch head score (Gemma)0.639
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Scholarly communication, Open science, Research integrity
Consensus categoriesMetaresearch, Bibliometrics, Scholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1500.639
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0860.204
Science and technology studies0.0010.000
Scholarly communication0.1260.119
Open science0.0090.002
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 head0.569
GPT teacher head0.598
Teacher spread0.029 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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