The Inception of an International Grand Committee
Bibliographic record
Abstract
Many issues studied by parliaments cross borders and boundaries. Concern about a major data breach involving social media users prompted similar parliamentary committee studies in both Canada and the United Kingdom. Information exchanged between the two committees and their willingness to work together paved the way for the inception of an International Grand Committee (IGC) – a series of meetings held by existing national-level parliamentary committees where parliamentarians from other countries are invited to participate. In this article, the authors outline the process to create the IGC, summarize two IGC meetings, and present comments on the IGC’s work by three Canadian parliamentarians who participated in these meetings. They conclude by noting the IGC meetings enabled parliamentarians from various countries to work together on issues of shared concern and importance, using existing national parliamentary committees as hosts and conduits for these international meetings; this structure differs from the work of multilateral interparliamentary assemblies.
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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 | 0.057 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".