Why implement plug-in electric policies? Comparing policy discourse in newspapers across three Canadian provinces (2008-2018)
Bibliographic record
Abstract
Governments can implement a wide range of policies to increase the uptake of plug-in electric vehicles (PEVs). For example, regions differ in their focus on demand-focused policies that encourage consumers to purchase a PEV, versus supply-focused policies that encourage the industry to develop or sell PEVs. I explore how policy discourse, or how language is used to create meaning around policy issues, can shed light on policy implementation in the Canadian provinces of British Columbia, Ontario, and Québec during the decade 2008-2018. In Canada, Québec became the first to use supplyfocused policy, while British Columbia and Ontario relied on demand-focused policies. Using a selection of 984 newspaper articles, I adopt a mixed-method approach to analyze statements from governments and other actors. First, I conduct (quantitative) content analysis and analyze the frequencies of frames (selected aspects of reality) around PEVs and policies. Second, I conduct (qualitative) discourse analysis by investigating how frames unite to create meaning in simplified stories, storylines. Similar frames occurred in all three case studies: governments framed PEV policy to meet climate goals while emphasizing PEVs’ private benefits to consumers. Policy discourses differed by regions: Québec’s emphasized PEVs as part of economic independence;Ontario’s demonstrated more policy controversy; and British Columbia’s remained silent over supply-focused policies during the time period. In British Columbia and Québec, the automobile industry favored a demand-focused policy approach. While this study remains exploratory, analyzing and comparing policy discourses can shed light on why policymakers in different regions may gravitate towards different policy approaches over time.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".