Bibliographic record
Abstract
Abstract The current study examines public accountants' professionalism and professional commitment (PC) and their effect on job performance. Results provide support for four of five dimensions of Hall's (1968) professionalism framework (beliefs in professional affiliation, professional dedication, self-regulation, and social obligation) and Meyer et al.'s (1993) three-dimensional PC framework (affective, continuance, and normative professional commitment) for modeling public accountants. Support was also found for most of the hypothesized relationships between professionalism and PC. Beliefs in professional affiliation, professional dedication, and self-regulation positively influenced affective professional commitment (APC). Belief in professional affiliation was negatively influenced by continuance professional commitment (CPC) but positively influenced by normative professional commitment (NPC). Belief in social obligation was also positively influenced by NPC. As expected, professionalism and PC were associated with job performance. Professionalism had an incremental effect beyond PC on job performance and as well, PC had an incremental effect over professionalism on job performance. Identifying relationships between professionalism and professional commitment with desirable outcomes is important for justifying future investments in the public accounting profession. Understanding these issues will assist in determining the types of professional attributes and commitments that are and should be fostered by the accounting profession.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".