Supporting and Supplemental Learning Intervention Strategies (SSi) In Introductory Accounting Classes
Bibliographic record
Abstract
The relevance of Business school education, especially the expectations of graduates' business decision making skills in the workplace, is under scrutiny (Tushman, O'Reilly, Fenollosa, Kleinbaum, & McGrath, 2007).Labour markets are demanding that graduates of business schools acquire critical numeracy skills that can provide a "seamless transition" to the workplace, yet a common refrain in recent scholarship, especially when it comes to management education, is that the business community's needs are not being met (Hughes, Tapp, & Hughes, 2008;Tushman et al.;Currie & Knights, 2003;Ulijn, 2000).Introductory accounting courses constitute a key foundation of business school graduates' competencies and as such they are fundamental in building the tools for business decision making and critical thinking skills (Etter, Burmeister & Elder, 2000) Part of the problem, according to Dehler, Welsh and Lewis (2001), can be attributed to the rooted historical trend towards teaching students to learn management decision making principles "as a set of content' areas,".As a result, it may be denying the inherent Journal of Accounting and Auditing: Research & Practice
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".