Benchmarking and Self-Assessment for Parliaments
Bibliographic record
Abstract
With international focus on good governance and parliamentary effectiveness, a standards-based approach involving benchmarks and assessment frameworks has emerged to evaluate parliament's performance and guide its reforms. The World Bank's has been a leader in the development of these frameworks, stewarding a global multi-stakeholder process aimed at enhancing consensus around parliamentary benchmarks and indicators with international organizations and parliaments across the world. \n \nThe results so far, some of which are captured in this book, are encouraging: countries as diverse as Australia, Canada, Ghana, Sri Lanka, Tanzania and Zambia have used these frameworks for self-evaluation and to guide efficiency-driven reforms. Donors and practitioners, too, are finding the benchmarks useful as baselines against which they can assess the impact of their parliamentary strengthening programs. The World Bank itself is using these frameworks to surface the root causes of performance problems and explore how to engage with parliamentary institutions in order to achieve better results. The World Bank can identify opportunities to help improve the oversight function of parliament, thus holding governments to account, giving 'voice' to the poor and disenfranchised, and improving public policy formation in order to achieve a nation's development goals. In doing so, we are helping make parliaments themselves more accountable to citizens and more trusted by the public.
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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 | 0.120 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".