A Plan for the People - A critical discourse analysis of the campaign platform and victory speech of Premier Doug Ford (2018-2019)
Bibliographic record
Abstract
The recent success of populist politicians in liberal democracies has influenced scholars in the field of political communications to define and categorize populism. The term “populism” has become recognized as a matter of concern when debating the future stability of democracy. It has been used to explain unprecedented wins in recent politics, such as Donald Trump’s victory in the 2016 United States Presidential Election. (Lahiti, 2018) It has also been associated with various socio-political and cultural changes over the past decades, more recently, the 2015 Syrian Refugee Crisis. (Abdalla, 2017) Populism is recognized as elusive, episodic, and relatively versatile in liberal democracies. (Mudde, 2004) This proposes the question of how has populist rhetoric become so effective in contemporary politics? According to Mudde (2004), populism is a thin-centered ideology that, "considers society to be ultimately separated into two homogeneous and antagonistic groups, ‘the pure people' versus ‘the corrupt elite,' and which argues that politics should be an expression of the volonté générale (general will) of the people.” (p.543) This definition is interpreted that within society, there are two distinct groups that coexist, ‘the people' and ‘the elite.' Mudde’s definition of populism will be used in this research, as the division of two homogeneous and antagonistic groups is prevalent in the case of Ontario. Moreover, as it will be discussed further in this research, the process of identifying who ‘the people' and ‘the elite’ are is central to creating divisive groups in Ontario.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".