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Record W3121489284 · doi:10.1111/sjoe.12225

Court Efficiency and Procurement Performance

2017· article· en· W3121489284 on OpenAlex
Decio Coviello, Luigi Moretti, Giancarlo Spagnolo, Paola Valbonesi

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueScandinavian Journal of Economics · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsHEC Montréal
FundersVetenskapsrådetUniversità degli Studi di PadovaCanada Research ChairsAgence Nationale de la Recherche
KeywordsInefficiencyProcurementPaymentOrder (exchange)BusinessWork (physics)Simple (philosophy)Law and economicsEconomicsMicroeconomicsFinanceMarketingEngineering

Abstract

fetched live from OpenAlex

Abstract Disputes over penalties for breaching a contract are often resolved in court. A simple model illustrates how inefficient courts can sway public buyers from enforcing a penalty for late delivery in order to avoid litigation, thereby inducing sellers to delay contract delivery. By using a large dataset on Italian public procurement, we empirically study the effects of court inefficiency on public work performance. Where courts are inefficient, we find the following: public works are delivered with longer delays; delays increase for more valuable contracts; contracts are more often awarded to larger suppliers; and a higher share of the payment is postponed after delivery. Other interpretations receive less support from 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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.037
GPT teacher head0.221
Teacher spread0.184 · 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