Bibliographic record
Abstract
Many countries are considering the formation of Parliamentary Budget Offices to improve transparency in the budgetary process. They face stiff resistance from key political stakeholders. The divergence of opinion between PBOs and other branches of government has at times put the very existence of the institution at risk, and the very credible threat of reprisals by other governmental institutions through funding cuts, staff removal, or outright institutional abolishment have hung over PBOs like a perpetual Sword of Damocles. In order to promote collaboration among Parliamentary Budget Officers a conference was held in Montreal in June 2013. It consisted of a comprehensive series of lectures, workshops, group reflections, case clinics and debates that allowed participants to coalesce into an extremely active and highly motivated community. The PBO delegates to the seminar agreed to form a symbiotic group, henceforth known as the Global Network of Parliamentary Budget Officers (GNPBO), that would allow for dynamic information-sharing between members using a variety of cutting edge tools and collaborative mechanisms. This article looks at the key role Canada played in the seminar and the establishment of the GNPBO.
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".