Research Openness in Canadian Political Science: Toward an Inclusive and Differentiated Discussion
Bibliographic record
Abstract
Abstract In this paper, we initiate a discussion within the Canadian political science community about research openness and its implications for our discipline. This discussion is important because the Tri-Agency has recently released guidelines on data management and because a number of political science journals, from several subfields, have signed the Journal Editors’ Transparency Statement requiring data access and research transparency (DA-RT). As norms regarding research openness develop, an increasing number and range of journals and funding agencies may begin to implement DA-RT-type requirements. If Canadian political scientists wish to continue to participate in the global political science community, we must take careful note of and be proactive participants in the ongoing developments concerning research openness.
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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 | 0.162 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.066 | 0.088 |
| Scholarly communication | 0.056 | 0.026 |
| Open science | 0.008 | 0.030 |
| Research integrity | 0.020 | 0.028 |
| Insufficient payload (model declined to judge) | 0.005 | 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".