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Record W6928984162 · doi:10.3886/e197843v1-163463

Data and Code for Organized Crime and Economic Growth: Evidence from Municipalities Infiltrated by the Mafia

2025· dataset· en· W6928984162 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueICPSR Data Holdings · 2025
Typedataset
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOrganised crimeLegitimacyReal estateIntervention (counseling)Economic analysisCode (set theory)Local governmentControl (management)

Abstract

fetched live from OpenAlex

This paper studies the long-run economic impact of dismissing city councils infiltrated by organized crime. Applying a matched difference-in-differences design to the universe of Italian social security records, we find that city council dismissals (CCDs) increase employment, the number of firms, and industrial real estate prices. The effects are concentrated in Mafia-dominated sectors and in municipalities where fewer incumbents are re-elected. The dismissals generate large economic returns by weakening the Mafia and fostering trust in local institutions. The analysis suggests that CCDs represent an effective intervention for establishing legitimacy and spurring economic activity in areas dominated by organized crime.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.046
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0040.007
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.068
GPT teacher head0.314
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