Democracy for sale : the marketization of Canadian political discourse and its implications for democratic citizenship
Bibliographic record
Abstract
An increasingly popular subject of focus within political science literature is the marketization of political discourse (Fairclough, 1995; Prince 2001; Simpson & Cheney, 2007). This article complements this body of literature by analysing how market-based discourse reinforces a passive frame of citizenship within Canadian politics. Market discourse utilizes concepts, values, and vocabularies commonly found in the marketplace – the language of branding, consumer satisfaction, efficiency and productivity – and applies it to the political realm. This paper argues that the marketization of political discourse frames politics as an area of social life predominantly concerned with the maximization of individual self-interest. In order to support this examination, political discourse analysis is combined with framing theory to analyse taxation discourse in party platforms from the 2011 Canadian federal election. Applying the frames to the party platforms reveals how market-based discourse reinforces a passive frame of citizens as self-interested, financially-motivated, and antisocial individuals. Marketization represents a worrisome trend in Canadian politics as it threatens to hollow out the public sphere by developing a consumption-oriented, self-interested civic culture.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.039 | 0.023 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".