Bibliographic record
Abstract
Abstract The article explains the role of the International Public Sector Accounting Standards Board (IPSASB) in setting accounting standards for the public sector, and the due process that is followed in setting those standards. The article explains the scope of the IPSASB's current project on social benefits, and how this compares to the scope of social benefits in Government Finance Statistics (GFS)/System of National Accounts (SNA) as well as the IPSASB's previous social benefits projects. The scope is wider than pensions, and wider than social security as social assistance is also included. The accounting principles that underpin the IPSASB's current project are discussed and include the IPSASB's definition of a liability, and the key role that a “past event” plays in that definition. This is contrasted with some of the actuarial approaches. The article then describes the potential past events that the IPSASB has considered to date in the project, and what impact liabilities from these past events would have on the financial statements. This comparison makes reference to pensions, where the financial impact of different past events will be greatest. The article sets out the IPSASB's proposals in its recent Exposure Draft ED 63, Social Benefits, and also discusses alternative views on recognition and measurement. The article concludes by discussing the IPSASB's current guidance in RPG 1, Reporting on the Long‐Term Sustainability of an Entity's Finances, and notes that the IPSASB is seeking views on whether it should undertake further work in this area.
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 | 0.020 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".