Comparative Analysis of the Relevance and Priority for Artificial Intelligence Tools, Services and Open Questions in the Hellenic, Argentinian and Canadian Parliaments
Bibliographic record
Abstract
Abstract Artificial Intelligence (ai) is on the rise and already affecting parliaments around the world. In the framework of a long-term and on-going research project, a series of interactive workshops have been organized between 2021 and 2023 in three national parliaments, in Greece, Argentina, and Canada, with the objective to assess the relevance and priority of a pre-defined set of 210 proposals, primarily regarding the use of ai-based tools and services in the parliamentary workspace. Reflection groups within each parliament evaluated these proposals providing invaluable results that can be utilized in manifold ways by the institutions, for instance towards structuring digital strategies, designing future it systems, or training intra-parliamentary stakeholders. This article presents a comparative analysis of the results obtained by all three parliaments. The analysis sheds light in a rapidly developing field of disruptive parliamentary technology (ParlTech) that with define the parliaments of the future.
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".