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Record W4210872871 · doi:10.1177/23996544211068295

Transnational circuits of policy knowledge and discursive migration. The formation and transformation of planning policies in Argentina

2022· article· en· W4210872871 on OpenAlexaff
Rodrigo Alves Rolo, Martijn Duineveld, Kristof Van Assche

Bibliographic record

VenueEnvironment and Planning C Politics and Space · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsUniversity of Alberta
FundersInstituto Nacional de Tecnología Agropecuaria
KeywordsCorporate governanceRationalitySociologyPublic relationsPolitical scienceEpistemic communityPublic administrationPolitical economyPoliticsManagementEconomicsLaw

Abstract

fetched live from OpenAlex

We analyse the migration of academic and policy discourses that contributed to (de)legitimise the formation of planning policies in Argentina since the 1950s. We focus on the communicative/collaborative rationality discourses emanating from Anglo-American academic circles that played a role in the revival of the Argentine planning system between 2004 and 2015. We adopt an evolutionary approach to policy travel and policy learning, deploying the concepts of discursive migration and discursive configuration to better understand how ideas, people and goods/resources reinvent themselves when transnationally circulating policy knowledge takes root locally. The migration process in Argentina led to the reinforcement of prevalent coordination mechanisms, redirecting concerns and conflicts into governance structures already existing, involving players already present and forms of expertise already dominant. The migrating collaborative discourse (self) transformed in relation to the receiving governance environment, becoming an effective compliance-gaining technique, while national actors found ways to engage and discipline provinces they depended on more than before.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.719
Threshold uncertainty score0.272

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)0.0000.000

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 head0.019
GPT teacher head0.266
Teacher spread0.246 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2022
Admission routes1
Has abstractyes

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