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The exploitation of public Brazilian airports under private regime: a review of government leased and permit grants

2013· review· en· W2109849940 on OpenAlexaff
Mello Fabiana Peixoto de, Dorieldo Luiz dos Prazeres

Bibliographic record

VenueJournal of Transport Literature · 2013
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsQueen's University
Fundersnot available
KeywordsLeaseGovernment (linguistics)Economic interventionismIntervention (counseling)BusinessDiversification (marketing strategy)Subject (documents)Public economicsEconomicsFinancePolitical scienceMarketingLawComputer science

Abstract

fetched live from OpenAlex

This paper investigates the regulatory aspects of exploiting Brazilian public airports under private regime. The proposition is that the essential difference between leased and permit grants is the level of regulatory intervention by the Government, which then determines the legal regime to which the airport exploitation is predominantly subject to. The descriptive and exploratory methodology used is based on previous literature on airport exploitation. The results indicate that airports exploited predominantly under private regime, that is, those granted permits, are generally subject to a lighter regulatory intervention than those exploited under public regime, which are granted a lease, hence the level of necessary regulatory intervention is the main criterion for determining which type of grant shall be used for a certain airport. The conclusion also indicates that the level of regulatory intervention to which an airport is subject will depend on the value of use to the collectivity that its operations generate. The optimal granting policy is an ideal combination of types of grants directly related to the diversification of the airport services and the different levels of regulatory intervention that they require.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.950
Threshold uncertainty score0.722

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.058
GPT teacher head0.273
Teacher spread0.215 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2013
Admission routes1
Has abstractyes

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