The Concept of Policy Departure: Understanding When and Why Political Parties and their Leaders Abandon Ideological Policy Objectives
Bibliographic record
Abstract
How do political parties and their leaders choose policy positions? Why and when do they choose to change them? This dissertation argues that one way to explain why political parties and their leaders abruptly change policy positions is to understand their need to build a winning electoral coalition. It posits that parties need to both mobilize core supporters and persuade swing voters to vote for them at the polls in order to win elections in majoritarian democracies. In order to bring together a winning coalition, this dissertation suggests that one means of doing so is to engage in what is referred to as policy departure. Through this method, parties and leaders will depart from ideological positions on issues that are not seen as core issues by a party’s base but may help win over swing voters. Such an approach allows them to appeal to swing voters without losing the support of the base, support that is necessary to build a winning electoral coalition. This dissertation puts forward a theoretical framework that posits that parties and their leaders make strategic decisions to engage in policy departure in three decision-making contexts: pre-election, post-election and critical moment. Different constraints are placed on decision-makers depending on the context in which those decisions are being made. The dissertation examines six core case studies, two for each of the three contexts. Core case studies come from Canada and focus on the Mulroney, Chrétien and Harper governments. Evidence in these studies was gathered from first-hand relational interviews. Secondary case studies, undertaken through process tracing, come from Canada, the United Kingdom and the United States. In this dissertation, the theory of policy departure is presented as a possible explanation when seeking to understand why political parties adopt policy positions that run counter to their baseline ideologies, as measured in prior election platforms. A clear method for understanding policy departure, both theoretically and in practice, is outlined. This method offers insight into how parties and leaders make strategic decisions, circumstances in which policy departure is likely to be successful, as well as the role of leaders in the decision-making process.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".