MétaCan
Menu
Back to cohort
Record W4362677196 · doi:10.1080/14702541.2023.2194285

Loss and Damage from climate change: legacies from Glasgow and Sharm el-Sheikh

2023· article· en· W4362677196 on OpenAlexfundno aff
W. Neil Adger

Bibliographic record

VenueScottish Geographical Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsnot available
FundersInternational Development Research CentreWellcome Trust
KeywordsDamagesClimate changeLoss and damagePolitical scienceGeographyLawForensic engineeringGeologyEngineering

Abstract

fetched live from OpenAlex

Conferences of the UN climate change convention have legacies both in formal outcomes and treaties and in raising the profile of emerging climate dilemmas. The joint legacies of COP26 in Glasgow and COP27 in Sharm el-Sheikh have been in elevating the profile and formalising the potential for solidaristic action on ‘Loss and Damage’ from climate change. This article reviews the documented outcomes on Loss and Damage from the two events to analyse the significance and constraints of this element of the overall climate change regime. Loss and Damage is likely to be constrained as a global collective action by the capacity to identify and measure losses and damages and by the ability of the climate change regime to deliver on meaningful resource transfers. Yet the formalisation of elements of climate justice through Loss and Damage is a real and lasting legacy of these COP events.

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)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0120.031
Scholarly communication0.0070.005
Open science0.0020.014
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0050.000

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 head0.078
GPT teacher head0.325
Teacher spread0.247 · 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
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

Citations20
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueScottish Geographical JournalSame topicClimate Change, Adaptation, MigrationFrench-language works237,207