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Record W4220708939 · doi:10.1007/s41782-022-00198-0

Scientists Against War: A Plea to World Leaders for Better Governance

2022· editorial· en· W4220708939 on OpenAlexaff
Seithikurippu R. Pandi‐Perumal, Velayudhan Mohan Kumar, Ganesh N. Pandian, Joop de Jong, Sudalaikannu Andiappan, Alexandru Corlăteanu, Arehally Marappa Mahalaksmi, Saravana Babu Chidambaram, Ramasamy Rajesh Kumar, Chellamuthu Ramasubramanian, Sudhakar Sivasubramaniam, Alvhild Alette Bjørkum, JosAnn Cutajar, Michael Berk, Ilya Trakht, Anton Vrdoljak, Miguel Meira e Cruz, Harris A. Eyre, Janne Grønli, Daniel P. Cardinali, Andreas Maercker, Willem van de Put, Jaswant Guzder, Bjørn Bjorvatn, Wietse A. Tol, Darı́o Acuña-Castroviejo, Marie Meudec, Charles M. Morin, Markku Partinen, Corrado Barbui, Mark J. D. Jordans, Mario H. Braakman, Christine Knaevelsrud, Ståle Pallesen, Marit Sijbrandij, Diego A. Golombék, Colin A. Espie, Pim Cuijpers, Hernán Andrés Marín Agudelo, Koos van der Velden, Bessel A. van der Kolk, Stevan E. Hobfoll, W.L.J.M. Devillé, Dieter Riemann, John Axelsson, Gloria Benítez‐King, Robert Macy, Vitalii Poberezhets, S. Ratnajeevan H. Hoole, Rangaswamy Srinivasa Murthy, Thomas Hegemann, Andreas Heinz, J Salvage, Alexander C. McFarlane, Rob Keukens, Harendra de Silva, Cornelia Oestereich, Jochen Wilhelm, Michael von Cranach, Klaus Hoffmann, Matthias Klosinski, Dinesh Bhugra, Mary V. Seeman

Bibliographic record

VenueSleep and Vigilance · 2022
Typeeditorial
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsUniversité LavalUniversity of TorontoJewish General Hospital
Fundersnot available
KeywordsPleaCorporate governancePolitical scienceEnvironmental ethicsLawManagementPhilosophyEconomics

Abstract

fetched live from OpenAlex

Contains fulltext : 251448.pdf (Publisher’s version ) (Closed access)

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)0.012
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.033
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.042
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0030.003
Science and technology studies0.0090.006
Scholarly communication0.0180.013
Open science0.0050.004
Research integrity0.0330.039
Insufficient payload (model declined to judge)0.0220.026

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 head0.049
GPT teacher head0.416
Teacher spread0.367 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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
Published2022
Admission routes1
Has abstractyes

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