MétaCan
Menu
Back to cohort
Record W3113766849 · doi:10.1163/15718069-25131254

“Paved with Good Intentions:” Best Practices in the Ethics of Track Two Interventions

2020· article· en· W3113766849 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Negotiation · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsConfidentialitySecrecyAccountabilityEngineering ethicsSet (abstract data type)Face (sociological concept)Intervention (counseling)Psychological interventionPolitical scienceSociologyField (mathematics)Public relationsNegotiationBest practiceLawPsychologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

Abstract This article unpacks the development of key ideas and debates which surround the ethical issues of Track Two. It defines what is meant by ‘Track Two’ and discusses how ethics might best be applied in practice to these dialogues. The ethical dimensions of four key issues are explored: accountability; the basis on which third parties feel they are entitled to intervene; the problem of dealing with actors who have committed atrocities; and ethical questions surrounding secrecy or confidentiality which is often required. The article suggests several ways forward in terms of creating a mechanism to enable practitioners to assist each other with the challenges they face. The article takes the view that a ‘hard and fast’ set of ethics may not be appropriate for the field, as each intervention is quite different, but rather that a set of ‘reflective questions’ should be developed to help practitioners confront ethical issues.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.861
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.173
GPT teacher head0.445
Teacher spread0.272 · 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