Factors That Affect Understanding of Social Responsibility Accounting
Bibliographic record
Abstract
Many social responsibility/sustainable development (SR/SD) issues affecting accounting policies and standards will have to be addressed by present and future accountants. This paper investigates qualitative factors that may impede the learning of, and attitudes toward, SR/SD. While Gordon (1998) examined exposure to SR/SD, the present study contributes to the literature in several ways. First, to overcome one of the limitations of Gordon's study, noted by her, matched pair responses (n = 198) to pre- and post-study questionnaires are employed in this study. These responses are analyzed using t-statistics, cluster analysis, and multivariate analysis. Second, three factors not previously examined that may affect learning of SR/SD (number of economics courses taken, gender, and grade point average) are explored in this paper. The positive conclusion is that exposure to SR/SD had more influence on learning, understanding, and attitudes than did pre-existing demographic and educational background variables with the exception of grade point average. As a surrogate for intelligence or ability to learn, grade point average was found to be highly significant in a multivariate model. An appreciation that ability to learn affects understanding and attitudes is important for instructors in both continuing professional education and university/college accounting.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".