Streamlining Canadian parliamentary data access: A user-friendly R package
Bibliographic record
Abstract
This paper focuses on the methodological and empirical challenges researchers encounter when accessing government open data through the case study of Canada's Open Government Action Plan, with a specific emphasis on datasets hosted by the House of Commons. To address these challenges, we have created an R package designed to streamline the retrieval process of datasets, that are not-so-user-friendly, from the House of Commons website. Furthermore, we have made complete datasets available in both French and English, which are the official languages of Canada, and in multiple formats to improve accessibility. Our package aims to be an invaluable resource for researchers interested in Canadian politics or conducting comparative research. Therefore, a portion of this paper is allocated to showcase the potential utility of our package. Through our research, we highlighted three crucial lessons: firstly, the heterogeneous nature of datasets requires flexibility and adaptability; secondly, open data curators encounter various challenges in addressing user-reported issues; and thirdly, there is a nuanced understanding of "openness" in government datasets. In conclusion, we reflect on the potential scalability of open data initiatives while advocating for a nuanced approach that considers the complex challenges associated with open data accessibility.
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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 | 0.023 | 0.137 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.051 | 0.027 |
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".