Reflections from parliamentarians and practitioners
Bibliographic record
Abstract
Parliaments are a political arena and hence exploring issues such as the politics of evidence use, the boundaries between politics and evidence use, and also the boundaries between various types of evidence becomes a necessary endeavor.Such an exploration illustrates the complexities of the work of parliaments when it comes to evidence use.It can assist in uncovering the ways in which the 'offer side' of evidence use, that of capacity-building, and the 'demand side' for evidence use, with the structures and advocacy activities that are deployed for institutionalised and system-wide thinking on evidence use in parliaments can be enhanced.As representatives of their constituents, Parliamentarians are compelled to push for honest, localised evidence of population needs and evidence of the efficacy of those policies designed to address social and development issues.In their exercise of oversight of the administrative branch, parliamentarians are expected to consider evidence ranging from administrative data, evaluation reports and statistics and anecdotal stories from citizens and media.At the same time, the realities within which parliamentarians operate also means that they may not be natural lovers of evidence.For them, 'evidence' may become a synonym for 'threat': what if a constituent realises that actions are not taken, results are not attained?But evidence may also be used to their personal advantage: here is a way to prove that I am working for my constituents.In either case, a parliamentarian might not stay neutral and reflect upon the way to best use the evidence in the decision-making process of his or her legislative functions.Reflecting on how to best to systematically feed evidence into parliaments which as spaces of political contestation, is a necessary and essential part of ensuring a healthy democracy and good governance.
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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 | 0.073 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.044 | 0.043 |
| Scholarly communication | 0.033 | 0.025 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.038 | 0.055 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".