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Record W4233663655 · doi:10.4324/9780203863398

Economic Growth, the Environment and International Relations

2010· book· en· W4233663655 on OpenAlexaff
Stephen James Purdey

Bibliographic record

Venuenot available
Typebook
Languageen
FieldEnvironmental Science
TopicSustainable Development and Environmental Policy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEconomic geographyEconomics

Abstract

fetched live from OpenAlex

The ubiquity of the commitment to economic growth, which Purdey refers to as the growth paradigm, is extraordinary. National governments around the world are seized of the same objective. Major international institutions such as the UN, the WTO, the World Bank, IMF and OECD, powerful international organizations such as regional trading blocs and multinational corporations – even civil societies of all kinds enthusiastically pursue a larger economic pie. This book examines the deep origins and rise to prominence of the commitment to economic growth. It explains why, despite the diversity of regime types, levels of development, cultures and other divisions typical of international relations, all major actors in the modern global polity pursue an identical political priority. Purdey critically examines the growth paradigm highlighting its normative foundations and its environmental impact, especially climate change. Using a neo-Gramscian approach, Purdey re-engages the ‘limits to growth’ controversy, identifying the commitment to growth as a form of utopianism that is as dangerous as it is seductive. By illuminating and interrogating the history, politics and morality of the growth paradigm, this book shifts the terrain of the limits debate from instrumental to ethical considerations. It will be of interest to students and scholars of political economy, international relations, environmental studies and ethics.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.006
Scholarly communication0.0060.005
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.003

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 head0.005
GPT teacher head0.185
Teacher spread0.179 · 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 designTheoretical or conceptual
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

Citations39
Published2010
Admission routes1
Has abstractyes

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