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Record W7161774296 · doi:10.82308/39544

Explaining why cities participate in global environmental governance: a comparison of Montréal, Bonn, and Cape Town

2021· dissertation· en· W7161774296 on OpenAlexaboutno aff
Luka Aubin-Jobin

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicCross-Border Cooperation and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsDiplomacyPremiseObligationGlobal cityAction (physics)Environmental movementEnvironmental studiesService (business)Environmental governance

Abstract

fetched live from OpenAlex

This thesis focuses on the motivations for cities to participate in global environmental diplomacy. It starts from the premise that cities have no obligation to pursue an international environmental agenda. Nevertheless, some do play leadership roles and demonstrate the importance and capacity of cities in the fight against climate change and environmental degradation. As cities' impacts on the environment are increasing, it is essential to understand how to steer them towards ambitious environmental actions. Drawing on constructivist theories of international relations, this thesis takes an abductive approach and argues that cities are motivated to participate in environmental diplomacy primarily by international recognition of their actions. By highlighting their capacity to tackle climate change, cities seek to showcase themselves but also to promote the role of cities more generally as legitimate actors in international politics. In service of this argument, I utilize comparative case studies of three cities: Montréal, Bonn, and Cape Town. This case selection maximizes diversity and addresses gaps in the extant urban diplomacy literature around mid-sized cities and cities in the Global South. I draw on data from official documents, city press releases, and four interviews with city officials. The results demonstrate that international recognition is an effective tool with which International Organizations can coordinate cities toward ambitious environmental action

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.353
Teacher spread0.330 · 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
Published2021
Admission routes1
Has abstractyes

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