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Record W2129137558 · doi:10.1186/s40463-014-0047-1

The Canadian contribution to the otolaryngology literature: A five year bibliometric analysis

2014· article· en· W2129137558 on OpenAlexaffabout
Joshua Gurberg, June RJ Lin, Elaheh Akbari, Paul White, Desmond A. Nunez

Bibliographic record

VenueJournal of Otolaryngology - Head and Neck Surgery · 2014
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsStornoway Diamond (Canada)University of British Columbia
Fundersnot available
KeywordsOtorhinolaryngologyBibliometricsRegional scienceLibrary scienceGeographyPolitical scienceMedicineComputer scienceSurgery

Abstract

fetched live from OpenAlex

OBJECTIVES: To assess the 2008-2012 Canadian contribution to the Otolaryngology literature. METHODS: All articles published from January 2008 - December 2012 in 5 Otolaryngology journals were reviewed. Nationality, number of authors, and study type were extracted. The output, number of authors, and study type of Canadian papers were compared to International papers using Mantel-Haenszel Common Odds Ratio Estimate, Pearson's Chi-Squared or Fishers exact tests. RESULTS: 4519 papers were analyzed. There was a statistically significant decrease in Canadian authored papers from 12.8% in 2008-9 to 10.2% in 2011-12 (Fishers exact, p = .01). Multi-authorship increased in Canadian papers (χ2, p = .01). The types of studies published by Canadian Otolaryngologists did not change over the study period. CONCLUSIONS: Canadian authored papers in a sample of Otolaryngology journals decreased from 2008 to 2012. The increase in multiauthorship, whilst indicating increasing collaboration, suggests reduced per capita publication productivity. These findings warrant further study.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.968
Threshold uncertainty score0.514

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.050
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.1110.157
Science and technology studies0.0030.001
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.111
GPT teacher head0.418
Teacher spread0.307 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations2
Published2014
Admission routes2
Has abstractyes

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