Bibliographic record
Abstract
ملخص: تتناول هذه المقالة دراسة حول تجربة كندا في تبني معايير التقارير المالية الدولية (IAS/IFRS)، باعتبارها من بين الدول الرائدة في المجال المحاسبي وتنظيم السياسة المحاسبية، بالإضافة إلى تاريخها العريق والناجح في تكوين الهيئات والجمعيات المهنية المشاركة في مختلف الاتحادات والتحالفات المحاسبية في مختلف أنحاء العالم. لذلك سنحاول من خلال هذا المقال التعرف على المبادئ المحاسبية المقبولة عموماً في كندا PCGR، ودراسة المخطط الإستراتيجي المنظم والمدروس الذي أعده مجلس معايير المحاسبة (CNC) في كندا للانتقال لتطبيق معايير التقارير المالية الدولية، والوقوف على أوجه الاختلاف والاتفاق بين المبادئ المحاسبية المقبولة عموما في كندا ومعايير التقارير المالية الدولية. Abstract: This article addresses to study the Canadian experience in adopting the International Financial Reporting standards (IAS/IFRS), because CANADA is one of the leading states in accounting field and the setup of accounting policy, without forgot its long and successful history in carving the bodies and professional organizations, participants in numerous and sundry unions and associations across the world. So we'll try knowing in this research the generally principles accounting accepted in CANADA, and study the arranged strategic Plan prepared and adjusted by the Canadian accounting standards board to switch to the application of international financial reporting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.210 | 0.153 |
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".