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Record W4386839272 · doi:10.19088/ids.2023.047

Landscapes of (In)justice: Reflecting on Voices, Spaces, and Alliances for Just Transition

2023· report· en· W4386839272 on OpenAlexfundno aff
Peter Newell, Roz Price, Freddie Daley

Bibliographic record

Venuenot available
Typereport
Languageen
FieldSocial Sciences
TopicTurkey's Politics and Society
Canadian institutionsnot available
FundersEuropean Investment BankEuropean CommissionUnited Nations Development ProgrammeCommission for Environmental CooperationUnited Nations Educational, Scientific and Cultural OrganizationInternational Labour OrganizationEnvironmental Defense FundInter-American Development BankAfrican Development Bank GroupFord Foundation
KeywordsWork (physics)PoliticsAction (physics)Economic JusticeTransition (genetics)Resource (disambiguation)Public relationsRepresentation (politics)Political scienceSociologyEnergy transitionSocial justicePolitical economyEngineeringLaw

Abstract

fetched live from OpenAlex

Just transitions seek to ameliorate the social and economic impacts of the global energy transitions that are essential to building an equitable low-carbon economy in the coming decades. Diverse groups of citizens need to be engaged in the design and implementation of transition policies across all scales and sectors for them to succeed and be socially acceptable. But how? And what lessons can we take from emerging practice to guide future action? This paper identifies insights from these experiences and struggles as they might pertain to contemporary attempts to ensure transitions are more socially just by drawing on a broad body of work on: (i) just transitions, energy justice, and energy transitions; (ii) contentious resource politics and attempts to democratise them; and (iii) social movement struggles for justice in the settings on which our project focuses. We focus on issues of voice (representation), spaces (for participation), and alliances (for change), paying particular attention to the three countries at the centre of this work: Colombia, Mozambique, and Nigeria.

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)0.002
metaresearch head score (Gemma)0.001
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: Other · Consensus signal: none
Teacher disagreement score0.843
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.245
GPT teacher head0.486
Teacher spread0.241 · 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
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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