Transparency and accountability in infrastructure stimulus spending: A comparison of <scp>C</scp>anadian, <scp>A</scp>ustralian and <scp>U</scp>.<scp>S</scp>. programs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In response to the global financial crisis that began in late 2007, many countries took on significant levels of deficit financing in order to increase spending on public works and infrastructure. This rapid infusion of public funding has raised concerns about the accountability and transparency of stimulus measures, including how best to monitor and evaluate the allocation and impact of the funds and report back to citizens. While there is growing research on the macro‐economic impacts of stimulus spending, very little comparative work has been done on the approaches of different countries to the governance of infrastructure stimulus spending programs. This article focuses on the latter by identifying and explaining the different practices in C anada, A ustralia and the U nited S tates in order to highlight implications for future stimulus‐led investment.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it