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Record W2999750709 · doi:10.1177/17835917211012326

Which access to which assets for an effective liberalization of the railway sector?

2021· preprint· en· W2999750709 on OpenAlexaff
Patrice Bougette, Axel Gautier, Frédéric Marty

Bibliographic record

VenueCompetition and Regulation in Network Industries · 2021
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsCenter for Interuniversity Research and Analysis on Organizations
Fundersnot available
KeywordsLiberalizationCompetition (biology)BusinessIndustrial organizationMarket accessStock (firearms)Complementary assetsEx-anteInternational tradeMarket economyEconomicsEngineering

Abstract

fetched live from OpenAlex

In the European rail industry, to enable competition in the market, entrants should be granted access to a large set of complementary services, beyond access to the tracks. For an efficient and effective entry, temporary access to quasi-essential complementary assets like rolling stock, mechanical maintenance workshops, data, schedules, etc. is required. In the liberalized rail sector, several observed anticompetitive practices involve distorted access to these quasiessential facilities. Therefore, competition agencies must deal with litigation between the incumbent and new entrants. Most cases have been settled, resulting in commitments from the incumbent. We argue that such transitory and case-by-case remedies fail to produce favorable conditions for a secure and efficient entry. Thus, we propose to systematize such remedies through asymmetric and enduring ex-ante regulation.

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)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.007
Scholarly communication0.0080.010
Open science0.0010.003
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0160.002

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 head0.035
GPT teacher head0.258
Teacher spread0.223 · 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 designNot applicable
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

Citations0
Published2021
Admission routes1
Has abstractyes

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