The Role of Management Accountant in Preparing Feasibility Studies
Bibliographic record
Abstract
This research is related to the role of management accountant in performing a feasibility study as an independent expert. The study explores the major aspects of this issue; it stated fifty questions that need to be answered in order to perform a feasibility study. These questions are related to project identification, environmental, legal, marketing, technical, production, organization, socio- economical, and financial evaluation aspects. The stages of performing a feasibility study have been analyzed and the corresponding tasks have been determined. Each group of tasks are related to one aspect and may produce the required output segment, which forms a part of the feasibility study. The stated list of tasks has been compared to the management accountant competencies as determined by the professional CAM exams in USA and Canada and other related research. It indicates that most of the required competencies to perform a feasibility study are already included. However, several major topics need to be considered for this purpose, such as topics related to environmental, marketing, and operation management aspects.
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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 | 0.106 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".