MétaCan
Menu
Back to cohort
Record W7021058312

Introduction

2020· book-chapter· en· W7021058312 on OpenAlexaboutno aff

Bibliographic record

VenueAcceda (Universidad de Las Palmas de Gran Canaria) · 2020
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsnot available
Fundersnot available
KeywordsAir traffic controlAviationMainland ChinaWhite paperChinaWork (physics)Government (linguistics)Private sector
DOInot available

Abstract

fetched live from OpenAlex

Aviation performance is an important cog in modern globalized economies, which demand flexibility, mobility, efficiency, and dependability. Airport delays have gone from being a nuisance to being a salient public concern, drawing the ire of even the White House. In this important book, international transportation experts compare and contrast how different nations have managed their airports and air traffic control systems and how well they are meeting the needs of their people. The book's cross-national approach encompasses several different institutional arrangements, making it a timely and valuable study in comparative political economy. Among the countries studied, the United States is sometimes seen as a bastion of free markets, at the forefront of airline deregulation, but its airports and air traffic control system are publicly owned and operated. The same is true in continental Europe, for the most part. In contrast, Australia, New Zealand, the United Kingdom, and Canada are experimenting with privatization, while even mainland China is allowing the private sector to participate in airport ownership. Which methods work best, and under what circumstances? This book provides the answers.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.787
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.034
GPT teacher head0.213
Teacher spread0.180 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueAcceda (Universidad de Las Palmas de Gran Canaria)Same topicAviation Industry Analysis and TrendsFrench-language works237,207