Nonpositivism in Behavioral Accounting Research: Initiated a Collaboration of Paradigm
Bibliographic record
Abstract
The purpose of this article is to explain how a paradigm can affect research results and how a paradigm apparently is no longer relevant in answering a practice, especially in behavioral accounting research. In addition, this article also want to describe what is the appropriate paradigm for development associated with behavioral accounting research. This article suggests that the failure of behavioral accounting research to narrow down between the research and practice of accounting due to the paradigm used in behavioral accounting research. Based on the characteristics of the behavioral accounting research interpretivism paradigm is more suitable. However, between positivism and interpretivism paradigm has advantages and disadvantages of each, and therefore the collaboration between the two is necessary.
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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 | 0.232 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.054 |
| Scholarly communication | 0.021 | 0.035 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".