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Record W2730117653 · doi:10.1080/21550085.2017.1342953

Two Concepts of Wrongful Harm: A Conceptual Map for the<i>Warsaw International Mechanism for Loss and Damage</i>

2017· article· en· W2730117653 on OpenAlexaff
Idil Boran

Bibliographic record

VenueEthics Policy & Environment · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Human Rights
Canadian institutionsYork University
Fundersnot available
KeywordsHarmNormativeMechanism (biology)Context (archaeology)Economic JusticeLaw and economicsEpistemologyMoral responsibilitySociologyPolitical sciencePsychologyLawPhilosophyHistory

Abstract

fetched live from OpenAlex

This paper is concerned with the moral concept of harm in the context of the Warsaw International Mechanism for Loss and Damage. This paper delineates between two concepts of wrongful harm: interactional versus architectural. It then examines these options with an eye toward developing a satisfactory normative approach for policy. While the interactional view of wrongful harm supports powerful arguments about moral responsibility, it has some clear limitations. This paper makes a case for the architectural view by underlining that it is not only compatible with the features of the Paris Agreement but also supports a distinctive conception of justice.

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)0.010
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.004
Science and technology studies0.0070.081
Scholarly communication0.0190.024
Open science0.0030.012
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0040.001

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 head0.086
GPT teacher head0.425
Teacher spread0.338 · 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 designTheoretical or conceptual
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

Citations7
Published2017
Admission routes1
Has abstractyes

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