Bibliographic record
Abstract
Citation (2014), "Editorial Advisory Board", Accounting in Latin America (Research in Accounting in Emerging Economies, Vol. 14), Emerald Group Publishing Limited, Bingley, pp. xi-xii. https://doi.org/10.1108/S1479-356320140000014013 Publisher: Emerald Group Publishing Limited Copyright © 2014 Emerald Group Publishing Limited Kamran Ahmed La Trobe University, Australia Catalin Nicolae Albu Bucharest Academy of Economic Studies, Romania Jahangir Ali La Trobe University, Australia Marcia Annisette York University, Canada Rebecca Boden University of Wales Institute Cardiff, UK Jui-Chin Chang Texas A&M International University, USA Susela Devi University of Malaya, Malaysia Charles Elad University of Westminster, UK Fábio Frezatti University of São Paulo, Brazil Zahirul Hoque La Trobe University, Australia Yusuf Khabari Cardiff University, UK Rihab Khalifa University of United Arab Emirates, United Arab Emirates Maria Krambia-Kapardis Cyprus University of Technology, Cyprus Keith Maunders University of Hull, UK Victor Murinde University of Birmingham, UK Deryl Northcott Auckland University Technology, New Zealand Joseph Onumah University of Ghana, Ghana Rudra Sensarma University of Hertfordshire, UK Michael Sherer University of Essex, UK Teerooven Soobaroyen University of Southampton, UK Venancio Tauringana Bournemouth University, UK Michael White University of the South Pacific, Fiji Danture Wickramasinghe University of Glasgow, UK Yan Xiong California State University, Sacramento, USA Haiyan Zhou The University of Texas – Pan American, USA Book Chapters Accounting in Latin America Research in Accounting in Emerging Economies Accounting in Latin America Copyright Page List of Contributors List of Reviewers Editorial Advisory Board About the Editors Introduction Accounting Information Quality in Latin- and North-American Public Firms Financial Reporting and Foreign Direct Investments in Latin America A Social Disclosure Index for Assessing Social Programs in Brazilian Listed Firms Auditors and the Foreign Corrupt Practices Act: Lessons from Latin America Relations between Supply Chain Performance Indicators Usage Patterns and Strategic Goals Typologies: Evidence from Brazilian Agribusiness Companies Performance Measurement System and Quality Management in Small and Medium-Sized Brazilian Enterprises About the Authors
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.114 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.022 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.016 | 0.010 |
| Insufficient payload (model declined to judge) | 0.278 | 0.255 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".