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Record W2076627995 · doi:10.1080/09557571.2014.960811

Hand in hand against climate change: cultural human rights and the protection of cultural heritage

2014· article· en· W2076627995 on OpenAlexaboutno aff
Sylvia Maus

Bibliographic record

VenueCambridge Review of International Affairs · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsnot available
Fundersnot available
KeywordsCultural heritageCultural rightsHuman rightsCultural heritage managementEnvironmental ethicsClimate changePolitical scienceCovenantContext (archaeology)NormativeFundamental rightsIndustrial heritagePolitical economySociologyLawGeographyArchaeology

Abstract

fetched live from OpenAlex

Within the debate on climate change and human rights, the field of culture, or cultural heritage in particular, plays a marginal role. At first glance, this seems reasonable, given the range of more concrete challenges people face in the context of climate change. However, the protection of cultural heritage is an important goal in its own right, even against the backdrop of other seemingly more pressing tasks. A human-rights-based approach to the debate on cultural heritage and climate change, it is argued, reinforces the international community's obligations to take necessary mitigation activities. Cultural rights and the corresponding duties, especially those under Article 15(1)(a) of the International Covenant on Economic, Social and Cultural Rights, have the potential to provide an effective additional normative basis for the protection of cultural heritage from the adverse consequences of climate change.

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)0.007
metaresearch head score (Gemma)0.008
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: none
Teacher disagreement score0.020
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.031
Scholarly communication0.0120.008
Open science0.0010.009
Research integrity0.0090.008
Insufficient payload (model declined to judge)0.0140.001

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 head0.060
GPT teacher head0.271
Teacher spread0.210 · 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

Citations28
Published2014
Admission routes1
Has abstractyes

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