DOUTORADO EM CONTABILIDADE: UM LEVANTAMENTO DAS PRINCIPAIS CARACTERÍSTICAS DE FORMAÇÃO EM UNIVERSIDADES DE LÍNGUA INGLESA
Bibliographic record
Abstract
The "sacrifices" to do a doctorate in accounting include: considerable period of time, opportunity cost, stress, financial costs and low attractiveness of the programs. This study seeks to identify the characteristics of training of doctors in accounts that are required for graduate programs in universities in countries whose official language is English. This research is descriptive, theoretical and conceptual, qualitative approach. The study adopts a secondary source of data collection and has inductive logic. The main results obtained: as prerequisites to pursue a doctorate, 44% of Canadian institutions, 29% of American institutions and 8% of the institutions in the UK applying previous knowledge of the candidates in the areas - accounting, business, microeconomics, skills mathematics (algebra, calculus, probability and statistics) in the UK, found that most institutions request the Test of English as a Foreign Language, 76% ask two letters of recommendation, 14% ask the General Management Admission Test / Graduate Record Examination, 17% ask the Grade Point Average and 24% carry out interviews with the candidates, in the United States the duration of the course is 4-5 years with full dedication (72%), Canada (44%). In Australia 75% and 33% of New Zealand programs are 3-4 years in dedication and up to eight years for part-time. We conclude that depending on the country that decides to do his doctorate in accounting features of formation change, but the future doctoral students can choose from 103 universities which program fits your profile.
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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 | 0.010 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".