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Record W4390355411 · doi:10.22495/cgobrv7i4sieditorial

Editorial: Pairing old with the new: Firm performance, ESG, and big data

2023· editorial· en· W4390355411 on OpenAlexaff
Azadeh Babaghaderi

Bibliographic record

VenueCorporate Governance and Organizational Behavior Review · 2023
Typeeditorial
Languageen
FieldEnvironmental Science
TopicBusiness and Economic Development
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPairingBusinessBig dataComputer scienceData miningPhysicsCondensed matter physics

Abstract

fetched live from OpenAlex

This issue revisits foundational concepts while presenting novel empirical evidence from developed and emerging markets examining corporate performance from multifaceted perspectives.

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)0.012
metaresearch head score (Gemma)0.049
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.024
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.049
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0060.003
Science and technology studies0.0060.004
Scholarly communication0.0120.006
Open science0.0060.002
Research integrity0.0240.027
Insufficient payload (model declined to judge)0.0180.016

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 head0.032
GPT teacher head0.220
Teacher spread0.188 · 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
GenreEditorial

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
Published2023
Admission routes1
Has abstractyes

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