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Record W2156033685 · doi:10.1080/00036840903389804

A nonparametric extension of generalized quadratic Box–Cox models in measuring technical efficiency

2010· article· en· W2156033685 on OpenAlex

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.

Bibliographic record

VenueApplied Economics · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicEfficiency Analysis Using DEA
Canadian institutionsUniversity of AlbertaMemorial University of Newfoundland
Fundersnot available
KeywordsNonparametric statisticsExtension (predicate logic)EconometricsVariance (accounting)Quadratic equationMathematicsStatisticsSample (material)Variable (mathematics)EconomicsComputer science

Abstract

fetched live from OpenAlex

This article uses a nonparametric extension of estimating Generalized Quadratic Box–Cox (GQBC) models using the Additivity and Variance Stabilization (AVAS) algorithm. The new method accounts for random noise in the data and relaxes the sensitivity of technical efficiency scores to the choice of functional form. It also provides more flexible choices for estimating the parameter of the dependent variable. The model is specified to measure technical efficiency scores of New York dairy producers in the period 1990 to 2000. Results show that the sample producers did not use resources efficiently, as the estimated mean technical efficiency score was found to be 0.663.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.074
GPT teacher head0.315
Teacher spread0.242 · 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