Divide and Conquer. Interest Group Engagement During Elections
Bibliographic record
Abstract
This thesis examines the electoral engagement of interest groups.While groups regularly interact with policymakers, elections provide a unique opportunity to shape the public debate and policy agenda of the future government.While these policy benefits are substantial, electoral engagement could also entail significant costs, and result in suboptimal relations with the next government.How do groups engage politically during elections and make strategic trade-offs?Based on an integrated approach-combining a cost-benefit framework and resource mobilization theory, this thesis develops a set of expectations concerning the electoral engagement of interest groups.Through a single case study analyzing 31 Canadian environmental interest groups (ENGOs) during its 42 nd national election in 2015, groups' electoral activities, policy focus and strategic trade-offs were analyzed.While the findings indicated that elections indeed constitute a circumstance with high stakes and substantial risks for interest groups, several groups demonstrate high levels of policy engagement, focusing on a variety of policy issues that often ask for policy change.At the same time, only a small set of groups succeeded in getting their policy asks covered in the media and acknowledged by political parties.In regards to strategic trade-offs a key finding was that cost-benefit calculations were found not to be exercised within ENGOs, but rather within networks of groups, in coordination with other national ENGOs. Divide and Conquer Interest Group Engagement During Elections
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".