Unrecognized assets created by public-sector investments in health and social services
Bibliographic record
Abstract
Purpose This paper analyzes two types of potential intangible public-sector assets for consideration by public-sector accounting boards. Government investments in health and social programs can create two potential intangible assets: the intangible infrastructure used to deliver the health or social program and the enhanced human capital embodied in the recipients of program services. Because neither of these assets is currently recognized in a government's year-end financial statements or broader general-purpose financial reports (GPFR), these reports may underrepresent the government's true fiscal and service capacity. Design/methodology/approach The paper uses an international accounting standards framework to analyze: whether investments in health and social programs create intangible assets that meet the definition of an asset as set out by International Public Sector Accounting Standards (IPSAS), whether they are assets of the government and whether they are recognizable for the purpose of financial reporting. Findings The intangible infrastructure asset created to facilitate the delivery of health and social programs would often qualify as a recognizable asset of the government. However, the enhanced recipient human capital asset created through the delivery of health and social programs would, in most instances, not qualify as a recognizable asset of the government, though there likely would be benefits from reporting on it through GPFRs or other mechanisms. Originality/value This paper makes two contributions. First, it identifies a previously overlooked intangible asset – the infrastructure created to facilitate the delivery of health and social programs. Second, it presents an argument regarding why, even when it fails to generate a recognizable intangible asset to government, it would be valuable for government to report such investments in supplementary statements.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".