Do imperfect budget policies lead to uneven year-end spending? The comparison of Ukraine & Canada
Bibliographic record
Abstract
Many organizations have budgets that expire at the end of the year and they may face \nincentives to rush to spend resources on projects at the year-end. We are testing this \nhypothesis using data from Ukraine’s and Canada’s state budgets for 2013-2017. Budget \nexpenditures for the last quarter of the fiscal year exceed the average and for the first quarter \nthey are lower than they should be on average. It is known that the in Ukraine a budget \npolicy became a state policy only in the 90s. Until that it was a part of the centralized \nbudget policy of the USSR. \nAfter the declaration of independence, Ukraine started to introduce the scientific \nsubstantiation and practical implementation of decisions and measures aimed at improving \nthe performance of a budget policy, so it is important to study the nature of this policy in \norder to use the positive experience of the developed countries.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".