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Record W4213369171 · doi:10.1038/s41558-022-01279-8

Decolonizing climate change–heritage research

2022· article· en· W4213369171 on OpenAlexafffund
Nicholas P. Simpson, Joanne Clarke, Scott Allan Orr, Georgina Cundill, Ben Orlove, Sandra Fatorić, Salma Sabour, Nadia Khalaf, Marcy Rockman, Patrícia Pinho, Shobha Maharaj, Poonam V. Mascarenhas, Nick Shepherd, Pindai M. Sithole, Grace W. Ngaruiya, Debra Roberts, Christopher H. Trisos

Bibliographic record

VenueNature Climate Change · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicConservation Techniques and Studies
Canadian institutionsInternational Development Research Centre
FundersUniversity of Cape TownNatural Environment Research CouncilInyuvesi Yakwazulu-NataliUniversity of PretoriaUniversity College LondonUniversity of ExeterSight Research UKUniversity of SouthamptonLeverhulme TrustUniversity of East AngliaGovernment of the United KingdomInternational Development Research CentreAfrican Academy of SciencesArcadia FundRoyal SocietyUniversidade de São PauloTechnische Universiteit DelftAarhus Universitet
KeywordsClimate changeCultural heritageAction (physics)Adaptation (eye)Environmental resource managementEnvironmental ethicsClimate change adaptationAction researchEnvironmental planningPolitical scienceGeographySociologyEnvironmental scienceArchaeologyOceanographyGeologyPsychology

Abstract

fetched live from OpenAlex

Climate change poses a threat to heritage globally. Decolonial approaches to climate change–heritage research and practice can begin to address systemic inequities, recognize the breadth of heritage and strengthen adaptation action globally.

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)0.042
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.042
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.042
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0080.026
Scholarly communication0.0090.012
Open science0.0030.018
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0160.002

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 head0.298
GPT teacher head0.382
Teacher spread0.084 · 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 designQualitative
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

Citations50
Published2022
Admission routes2
Has abstractyes

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