Canadian accountants: examining workplace learning
Bibliographic record
Abstract
Purpose This paper seeks to examine workplace learning strategies, learning facilitators and learning barriers of public accountants in Canada across three professional levels – trainees, managers, and partners. Design/methodology/approach Volunteer participants from public accounting firms in Nova Scotia and New Brunswick completed a demographic survey, a learning activities survey, a learning barriers survey, and a learning facilitators survey. Quantitative analysis provided total scores for key variables and compared these across the three levels. Findings The paper finds that accountants across different levels use a variety of formal and informal learning strategies, although informal strategies predominate. Accountants encounter numerous facilitators and barriers. There are variations in strategies, barriers and facilitators based on professional level; for example, trainees make more use of e‐learning than do either managers or partners. Research limitations/implications Future research could focus on the efficacy of accountants' formal and informal learning strategies as well as how e‐learning can be appropriately managed and utilized. Practical implications Allocation of work and relationships with people are important to the learning process and should be considered in work assignments. One implication is to encourage informal learning and provide appropriate learning activities and feedback so that informal learning is maximized. There could also be more emphasis placed on assisting partners and managers in developing their roles as coaches and mentors. Originality/value The paper provides information on workplace learning for an understudied group of professionals in a Canadian context.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".