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Record W1676849681

Fading Resilience? Creative Destruction, Neoliberalism and Mounting Risks

2014· article· en· W1676849681 on OpenAlexaff
Pierre Filion

Bibliographic record

VenueOpenEdition (OpenEdition) · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNeoliberalism (international relations)Resilience (materials science)Creative destructionFadingSociologyPolitical sciencePolitical economyEconomicsEngineeringNeoclassical economicsTelecommunicationsPhysics
DOInot available

Abstract

fetched live from OpenAlex

The paper argues that creative destruction at the heart of capitalist dynamics, along with risk-prone features of neoliberalism, impedes wide-ranging resilience. A form of resilience focussing narrowly on natural and human-caused disasters replaces broader responses to risks, which address economic and personal hardship. Concurrently, combined effects of neoliberal societal arrangements and economic globalisation exacerbate economic risks to which individuals and communities are exposed. A discussion of the shrinking city phenomenon demonstrates that economic hazards, against which most resilience measures are helpless, represent a peril that is more common than, and often at least as destructive as, the disasters targeted by mainstream resilience approaches. The experience of shrinking cities points to the dual impact of their contracting economies: direct threats to the wellbeing and survival of their residents, and a depletion of the intervention capacity of agencies responsible for different aspects of urban resilience. The paper closes with an examination of realistic means of enhancing resilience in the present neoliberal context.

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)0.005
metaresearch head score (Gemma)0.005
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 score0.012
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.067
Scholarly communication0.0120.009
Open science0.0010.014
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.028
GPT teacher head0.291
Teacher spread0.264 · 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

Citations13
Published2014
Admission routes1
Has abstractyes

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