Exploring the Use of Nodality Based Information PolicyTools by Canadian Electoral Agencies
Bibliographic record
Abstract
Despite a healthy number of studies examining the motivations or voting practices of Canadians, little comparative work examines communications activities of electoral agencies. The following article maps out such activities through an assessment of nodality (information-based) policy tools use by four Canadian electoral agencies (Elections Canada, Elections Ontario, Elections BC, Elections Quebec). The paper begins by situating information‐based policy tools within the broader policy tools literature. Subsequently, such tools are then classified with respect to their relationship to policy making activities at the ‘front-end’ (agenda setting and policy formulation) and ‘backend’ (policy implementation and evaluation) of the policy cycle. Upon analysis, a variety of instrument mixes are detected with an overall shift from broad sweeping substantive instruments, such as mass information campaigns towards targeted approaches, to increased partnerships aimed at reaching specific cohorts with historically lower levels of voter participation. Furthermore, instrument mixes are found to vary jurisdictionally with respect to the adoption of newer Internet based tools versus traditional tools. In general, all four cases are found to frequently rely on both procedural and substantive information‐based policy tools related to ‘back-end’ policy-making activities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".