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Participatory and Deliberative Approaches to Climate Change

2016· reference-entry· en· W2733223497 on OpenAlexaff
Gwendolyn Blue

Bibliographic record

VenueOxford Research Encyclopedia of Climate Science · 2016
Typereference-entry
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDeliberationPanacea (medicine)Political scienceClimate changeCorporate governanceDeliberative democracyCitizen journalismStakeholderClimate governancePoliticsEnvironmental planningSociologyEnvironmental resource managementEnvironmental ethicsPublic relationsDemocracyBusinessGeographyEconomicsLaw

Abstract

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Abstract Participation by citizens and stakeholder groups is an important aspect of climate governance at the regional, national, international, and global levels. Increasing awareness of anthropogenic causes of climate change has fueled calls for democratic action and renewal that promise to enrich both existing and emerging forms of political engagement. Participation is not a panacea, however, and has many limitations. Three substantial critiques of participatory and deliberative approaches to climate change hinge on questions of power, authority, and opportunities for dissent. The climate system itself poses unique challenges to democratic governance. Accelerating rates of environmental change associated with climate change make past experience less applicable to current situations and complicate predicting the future even further. As such, participatory and deliberative approaches may need to be reconfigured to respond adequately to the challenges of climate change. Systems approaches broaden the scope of participation and deliberation, and innovative participatory methods are increasingly moving beyond narrow framings of climate change. As deliberative and participatory initiatives become more common, it is no longer a question of supporting or rejecting participatory forms of climate governance. Rather, questions need to address what kinds of consequences will occur and in whose interests certain participatory processes operate. Which social views and values are supported and which are marginalized, and what are the consequences of collective responses to this pressing environmental and social issue?

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.005
Scholarly communication0.0000.001
Open science0.0020.005
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.247
GPT teacher head0.368
Teacher spread0.121 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations15
Published2016
Admission routes1
Has abstractyes

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