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Record W4306399106 · doi:10.1177/00207020221135364

Healing dialogue: Can the techniques and practices of Track Two diplomacy play a role in resolving public health conflicts?

2022· article· en· W4306399106 on OpenAlexaff
Aleem Bharwani, Julia Palmiano Federer, Jack Latour

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Security and Public Health
Canadian institutionsUniversity of OttawaUniversity of Calgary
Fundersnot available
KeywordsPublic healthPublic relationsDiplomacyPublic diplomacyPandemicPolitical scienceTrack (disk drive)Conflict resolutionSociologyCoronavirus disease 2019 (COVID-19)MedicineLawPoliticsComputer science

Abstract

fetched live from OpenAlex

Track Two Diplomacy, a form of facilitated informal and unofficial dialogues between conflicting parties, has become a well-established form of international conflict resolution. This paper seeks to explore whether the techniques and practices of Track Two could be applied in a new setting beyond international armed conflicts: public health. Global society continues to grapple with the devastating effects of the COVID-19 pandemic, systemic racism, and climate change, among other pressing public health issues that can not only exacerbate but also create new conflicts that negatively affect communities. Innovative and interdisciplinary approaches are needed more than ever. We synthesize literature from both Track Two and public health fields to present a conceptual framework that posits whether and how such concepts as the “problem-solving workshop,” “transfer,” “reflective practice” and others might support parties involved in divisive, intractable, visible, and invisible conflicts which currently mark the public health space.

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)0.008
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: none
Teacher disagreement score0.911
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.037
GPT teacher head0.409
Teacher spread0.373 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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