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Record W2760157135 · doi:10.1108/ijccsm-02-2017-0037

Seizing history: development and non-climate change in Small Island Developing States

2017· article· en· W2760157135 on OpenAlexaff
Godfrey Baldacchino

Bibliographic record

VenueInternational Journal of Climate Change Strategies and Management · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsOriginalityClimate changeSmall Island Developing StatesPsychological resiliencePolitical scienceLegitimationValue (mathematics)Work (physics)GeographyPolitical economyDevelopment economicsEconomic geographySociologyEconomicsPoliticsPsychologyLawComputer scienceEngineeringOceanography

Abstract

fetched live from OpenAlex

Purpose This paper offers a critical review of climate change related initiatives in small island states, including Small Island Developing States (SIDS), which can end up as ontological traps: fuelled and supported by external donor agencies, thwarting out-migration and shifting scarce and finite resources away from other, shorter-term and locally spawned development trajectories and objectives. Design/methodology/approach This paper is based on a selective literature review. It clusters important themes found in published research and policy documents. Findings The results identify a burgeoning critical voice in regards to resilience and its legitimation of climate change driven projects in SIDS. This paper recommends a more nuanced approach which also privileges migration. Originality/value This paper provided a critical overview and synthesis of the immobility implicit in much climate change related work, through the critical lens of island studies and post-colonial studies.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0040.011
Scholarly communication0.0070.005
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.199
GPT teacher head0.356
Teacher spread0.157 · 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 designObservational
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

Citations49
Published2017
Admission routes1
Has abstractyes

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Same venueInternational Journal of Climate Change Strategies and ManagementSame topicClimate Change, Adaptation, MigrationFrench-language works237,207