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Record W4319841864 · doi:10.1080/13597566.2022.2160975

De/centralization in Mexico, 1824–2020

2023· article· en· W4319841864 on OpenAlexaboutno aff
Juan C. Olmeda

Bibliographic record

VenueRegional & Federal Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado de São PauloLeverhulme TrustJames B. Pendleton Charitable Trust
KeywordsDecentralizationEliteIdeologyAuthoritarianismQuarter (Canadian coin)Political sciencePolitical economySociologyPublic administrationPoliticsLawHistoryDemocracy

Abstract

fetched live from OpenAlex

This article presents an analysis of de/centralization in Mexico during the period 1824–2020, building on an original dataset that coded three subdimension of the politico–institutional arrangement, 22 policy areas and 5 subdimension of the fiscal sphere for each year during that time. The country evolved from a decentralized federation at the outset to a relatively centralized one nowadays. The Mexican case also sheds light on the importance of regime type and the ruling elite's ideological orientation to explain de/centralization patterns. Centralization was prevalent during two long authoritarian periods since the last quarter of the XIX century. On the contrary, dynamic decentralization occurred once the authoritarian regime began to erode in the 1980s. The ideological orientation of the ruling elite helped to strengthen those trajectories. When that elite embraced developmental ideas, the move towards centralization was deeper, whereas the opposite took place once national authorities embraced a neoliberal agenda.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.074
GPT teacher head0.375
Teacher spread0.301 · 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 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

Citations26
Published2023
Admission routes1
Has abstractyes

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