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Record W3138768291 · doi:10.1093/neuros/nyab051

Developing a Professionalism and Harassment Policy for Organized Neurosurgery

2021· article· en· W3138768291 on OpenAlexaff
Ellen L. Air, Katie O. Orrico, Deborah L. Benzil, Alan M. Scarrow, James Bean, Catherine A. Mazzola, Linda M. Liau, James T. Rutka, Karin M. Muraszko

Bibliographic record

VenueNeurosurgery · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineSummitPublic relationsGuard (computer science)HarassmentInstitutionJurisdictionPublishingLawNursingPolitical science

Abstract

fetched live from OpenAlex

Annual conferences, educational courses, and other meetings draw a diverse community of individuals, yet also create a unique environment without the traditional guard rails. Unlike events held at one's home institution, clear rules and jurisdiction have not been universally established. To promote the open exchange of ideas, as well as an environment conducive to professional growth of all participants, the leading neurosurgical professional organizations joined forces to delineate the expectations for anyone who participates in sponsored events. The One Neurosurgery Summit Taskforce on Professionalism and Harassment developed a foundational policy that establishes common expectations for behavior and a unified roadmap for the prompt response to untoward events. We hope that publishing this policy will inspire other medical organizations to establish their own meeting and conference policies. More importantly, we wish to bring greater attention to everyone's responsibility for ensuring a safe and respectful space for education, scientific debate, and networking during organized events.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.092
GPT teacher head0.364
Teacher spread0.271 · 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 designNot applicable
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

Citations6
Published2021
Admission routes1
Has abstractyes

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