Communicating bad medicine: Making the argument for the adoption of the harmonized sales tax in Ontario, Canada
Bibliographic record
Abstract
In this dissertation, I explore the question of how a democratically elected government sells an unpopular concept, such as a new tax, to citizens. I interviewed politicians, political advisors, civil servants, and business leaders responsible for the introduction of Ontario’s value-added tax in 2010 (known as the Harmonized Sales Tax or HST) and examined print and media artifacts to answer this question. \n \nThis topic is important to political analysts and rhetoricians who need to introduce tax initiatives to create necessary revenue streams for their governments. As we have seen in the past, unpopular tax initiatives can be reversed (as occurred in British Columbia’s 2011 referendum on the HST) and governments can be defeated (as happened to the Manitoba government in a vote of non-confidence in 1988). An analysis of lessons learned from Ontario’s successful experience with the HST in 2010 is useful information for governments for any future tax or controversial policy communication. \n \nMy research is the first of its kind to examine the rhetoric used to introduce the HST in Ontario. The examples and interviews show how an effective argument was developed for the new tax. However, the methods alone do not tell us why these were successful: my research fills this gap and explains the “How?” and the “Why?” \n \nI discovered two effective rhetorical approaches that could be used to sell an unpopular tax: (1) use logical arguments to present information, and (2) demonstrate a deep commitment that the tax is the right thing. The Ontario government used logical and reasonable arguments to persuade businesses and citizens that the HST was the right thing to do. Because politicians and civil servants believed that the HST was the right thing to do and were dedicated to its introduction, the HST was implemented without issue and political fallout. \n \nThis topic informs the fields of technical communication, rhetoric, economics, and public sector communication.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.053 | 0.019 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".