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Record W4390109028 · doi:10.32872/cpe.13197

Harbingers of hope: Scientists and the pursuit of world peace

2023· editorial· en· W4390109028 on OpenAlexaff
Seithikurippu R. Pandi‐Perumal, Willem van de Put, Andreas Maercker, Stevan E. Hobfoll, Velayudhan Mohan Kumar, Corrado Barbui, Arehally Marappa Mahalaksmi, Saravana Babu Chidambaram, Per Olof Lundmark, Tual Sawn Khai, Lukoye Atwoli, Vitalii Poberezhets, Ramasamy Rajesh Kumar, Derebe Madoro, Hernán Andrés Marín Agudelo, S. Ratnajeevan H. Hoole, Luísa Teixeira-Santos, Paulo Pereira, Konda Mani Saravanan, Anton Vrdoljak, Miguel Meira e Cruz, Chellamuthu Ramasubramanian, Alvin Kuowei Tay, Janne Grønli, Marit Sijbrandij, Sudhakar Sivasubramaniam, Meera Narasimhan, Eta Ngole Mbong, Markus Jansson‐Fröjmark, Bjørn Bjorvatn, Joop de Jong, Mario H. Braakman, Maurice Eisenbruch, Darı́o Acuña-Castroviejo, Koos van der Velden, Gregory M. Brown, Markku Partinen, Alexander C. McFarlane, Michael Berk

Bibliographic record

VenueClinical Psychology in Europe · 2023
Typeeditorial
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Health and Medical Research Council
KeywordsEnvironmental ethicsPolitical scienceSociologyPhilosophy

Abstract

fetched live from OpenAlex

The ongoing wars in many regions-such as the conflict between Israel and Hamas-as well as the effects of war on communities, social services, and mental health are covered in this special editorial. This article emphasizes the need for international efforts to promote peace, offer humanitarian aid, and address the mental health challenges faced by individuals and communities affected by war and violence.

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)0.009
metaresearch head score (Gemma)0.038
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 score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.038
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0040.002
Science and technology studies0.0060.006
Scholarly communication0.0130.007
Open science0.0030.002
Research integrity0.0150.030
Insufficient payload (model declined to judge)0.0070.005

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 head0.273
GPT teacher head0.621
Teacher spread0.348 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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