A Central Bank's Guide to International Financial Reporting Standards
Bibliographic record
Abstract
About one-quarter of the world’s central banks apply IFRS with approximately a quarter more looking to IFRS for further guidance where their local standards do not provide enough guidance. Given the varied mandates and types of policy operations undertaken by central banks, there also exists significant variation in practice, style, and the extent of the financial disclosures in both the primary statements and in the note disclosures. By their nature, central banks are unique in their jurisdiction and so do not always have local practices and examples they can follow. Although the major accounting firms have created model disclosures intended for commercial banks, these are often not totally appropriate for a central bank. The application of IFRS across central banks differs based on the mandate of the central bank and the capacity of the accounting profession in the specific jurisdiction. An analysis of international practices, such as those undertaken in preparing these model statements, may help address questions about the structure of the statements themselves as well as the organization of the note disclosures. As a consequence, each central bank following IFRS has largely developed its own disclosures with only limited reference to others. Input from the external auditors has been significant, but some of this has been determined by the approach used by the specific auditor’s style for commercial banks rather than central banks. Auditors do not always fully appreciate the differences between a commercial bank and a central bank, which has a different role and undertakes transactions to meet its policy objectives. This has often led to an over emphasis of items not material in the context of a central bank and insufficient disclosures on operations or accountabilities specific to the functions of the central bank.
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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 | 0.054 | 0.117 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.086 | 0.131 |
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".