The usefulness of accrual-based surpluses in the Canadian public sector
Bibliographic record
Abstract
This paper investigates the usefulness of accrual-based surpluses for predicting future cash flows and surpluses in the context of the Canadian public sector. We provide evidence that surpluses incrementally enhance the ability of operating cash flows to predict future cash flows and surpluses. Analysis of our accrual quality model illustrates that accrual accounting is useful in the public sector for mitigating the noise in operating cash flows. We also find that decomposing surpluses into operating cash flows and accruals enhances the ability of surpluses to forecast future cash flows and surpluses, indicating that aggregate and disaggregated surpluses are positively related to both relevance and reliability. Our test results do not indicate the presence of conservatism in the Canadian public sector, and confirm that the usefulness of surpluses in making predictions is independent of selected control factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".