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Record W2764303703 · doi:10.1017/s0143814x17000174

Political land corruption: evidence from Malta – the European Union’s smallest member state

2017· article· en· W2764303703 on OpenAlexaff
Paul Caruana‐Galizia, Matthew Caruana-Galizia

Bibliographic record

VenueJournal of Public Policy · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLand Rights and Reforms
Canadian institutionsWorld Federation of Science Journalists
Fundersnot available
KeywordsLanguage changePoliticsState (computer science)Member stateEuropean unionZoningMember statesPolitical corruptionPolitical scienceBusinessInternational tradeLaw

Abstract

fetched live from OpenAlex

Abstract Political corruption in the land sector is pervasive, but difficult to document and effectively prosecute. This article provides new evidence on political land corruption in Malta, the European Union’s smallest member state and one of the world’s most densely populated countries. It shows how the country’s highly restrictive zoning laws, along with a de jure independent regulator, have created opportunities for extensive and endemic corruption in the granting of land development permits in zones that are outside development. It provides an example of governments creating institutions as rent-collection instruments – not to correct market failures, but to create opportunities for corruption. The unique underlying data set was collected through an automated web-scraping program as the regulator first turned down then ignored freedom of information requests for the data.

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)0.004
metaresearch head score (Gemma)0.021
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 score0.262
Threshold uncertainty score0.521

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0020.004
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.001
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 head0.088
GPT teacher head0.298
Teacher spread0.209 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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