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Record W2161698349 · doi:10.3233/wor-2010-0988

WORK: A historical evaluation of the impact and evolution of its editorial board

2010· article· en· W2161698349 on OpenAlexaff
Lynn Shaw, Birgit Prodinger, Karen Jacobs, Nathan M. Shaw

Bibliographic record

VenueWork · 2010
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsThe King's UniversityWestern University
Fundersnot available
KeywordsEditorial boardScholarshipWork (physics)Formative assessmentEngineering ethicsSociologyPerspective (graphical)Political sciencePublic relationsLibrary scienceComputer scienceEngineeringPedagogyLaw

Abstract

fetched live from OpenAlex

OBJECTIVES: A historical review of the editorial board and the founding editor of WORK: A Journal of Prevention, Assessment and Rehabilitation was conducted to examine the understanding of the editorship and contributions of this team to the knowledge in WORK. PARTICIPANTS: The team of four authors worked together to identify an approach to evaluate the contributions and impact of WORK's editorial board (EB) on the journal's scholarship. The editor-in-chief (EIC) and editorial board members were participants in this evaluation. METHODS: Informative and formative evaluations were used to investigate how knowledge was shaped through the development of an epistemic community of scholars in the field of work. Metrics of the EB composition and participation in the journal as well as surveys and interviews with the board and the editor-in-chief were analyzed. RESULTS: The EB represents an international community of scholars with a common interest in work and who contribute academically both within WORK and beyond. The epistemic community that has evolved through the editorial board represents a pluralistic perspective on work that is needed to inform practice, and knowledge. CONCLUSION: Future directions to continue to advance knowledge through WORK's editorial board and EIC are elaborated.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.287
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.110
GPT teacher head0.498
Teacher spread0.387 · 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
Published2010
Admission routes1
Has abstractyes

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