Bibliographic record
Abstract
Citation (2006), "List of Contributors", Arnold, V., Clinton, B.D., Luckett, P., Roberts, R., Wolfe, C. and Wright, S. (Ed.) Advances in Accounting Behavioral Research (Advances in Accounting Behavioural Research, Vol. 9), Emerald Group Publishing Limited, Bingley, pp. vii-viii. https://doi.org/10.1016/S1475-1488(06)09009-0 Publisher: Emerald Group Publishing Limited Copyright © 2006, Emerald Group Publishing Limited Book Chapters List of Contributors Reviewer Acknowledgments Editorial Policy and Submission Guidelines The impact of accountability on the processing of nondiagnostic evidence Auditors’ memory of internal control information: the effect of documentation preparation versus review Internal auditor burnout: an examination of behavioral consequences Fairness, budget satisfaction, and budget performance: a path analytic model of their relationships Understanding investment expertise and factors that influence the information processing and performance of investment experts The influence of outcome knowledge on judges and jurors’ evaluations of auditor decisions: A review and synthesis of prior research Why you should consider SEM: A guide to getting started A Postmodern Stakeholder Analysis of Telework
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 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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.703 | 0.678 |
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".