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Record W7001407137

Is the United Nations framework convention on climate change an effective (or appropriate) institution for supporting indigenous peoples' adaptation to climate change?

2015· dissertation· en· W7001407137 on OpenAlexafffund

Bibliographic record

VenueeScholarship@McGill (McGill) · 2015
Typedissertation
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsMcGill University
FundersInternational Development Research CentreMcGill University
KeywordsIndigenousClimate changeVulnerability (computing)InstitutionUnited Nations Framework Convention on Climate ChangeNegotiationAdaptation (eye)
DOInot available

Abstract

fetched live from OpenAlex

The United Nations Framework Convention on Climate Change, the core of today's global climate change regime, is an intergovernmental institution that was established in the 1990s with the objective of bringing nations together to negotiate policies in a global effort to stabilize greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous interference with the climate system.Today, with increasing scientific certainty regarding the current and future impacts of climate change, and the growing notion that some degree of change is unavoidable regardless of mitigation, the role of adaptation, and its scope, have expanded a great deal within the institution since it was first envisioned.It is unclear however what opportunities and barriers this discursive structure may create for adaptation support for vulnerable sub-national populations such as indigenous peoples.Accordingly, this study uses critical discourse analysis to examine the evolution of the embedded discourse on adaptation to the adverse effects of climate change in the institution based on the official decisions rendered by the Conference of the Parties, and seeks to expose the policy implications for indigenous peoples at different scales.Noteworthy trends identified in this study include an increasingly explicit recognition of the heightened vulnerability of indigenous peoples to climate change, and the gradual shift away from approaches that are purely scientific to approaches that value and include traditional and indigenous knowledge for adaptation where appropriate.Adaptation assistance provided by developed nations however, remains exclusively aimed at projects in developing country Parties.

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)0.006
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0080.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
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 head0.136
GPT teacher head0.369
Teacher spread0.233 · 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.

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

Citations0
Published2015
Admission routes2
Has abstractyes

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