How and Why Privatization is Still Widely Used as a Policy Tool in Canada: a Qualitative Study
Bibliographic record
Abstract
This study explores the policy process surrounding the decision to privatize and its effects on government, the public, labour, and the business community in Canada. Four case studies are looked at in two different sectors (waste collection and public transit) and two municipalities (The Greater Toronto Area (GTA) and the Greater Vancouver Regional District (GVRD)). The results were analyzed through a network/class theoretical framework using open-ended coding. Two main questions were asked: (1) Does privatization create winners and losers? and (2) How and why is privatization still used widely as a policy tool if there now exists a large body of evidence that suggest that it is a poor policy option? The study resulted in a number of findings related to both questions. In particular a number of important and telling variables emerged from the data. For the first question, it became clear that privatization did in fact produce winners and losers. More specifically, it became clear that there were clear winners and clear losers in the privatization equation. For these particular cases it was clear that the government, the public, and organized labour lost, while only the business community won. For the second question, the results showed that a number of factors influence the decision-making process surrounding privatization, and often times these factors are anything but empirical or evidence-based. The results showed that factors like ideology, political reasons, and network relationships played a key role in influencing policy, while factors like evidence, increased efficiency, and increased productivity were lacking or ignored. Overall, this study represents a contribution to the field of policy studies and the study of privatization. First, this ''''' dissertation represents a contribution to the small but growing body of critical approach literature that seeks to understand the effects of privatization. Second, it helps build the case for the network/class approach to be included as one of the more insightful approaches to understanding the policy process. Finally, it sheds new light on the policy and decision-making processes that surround privatization, which, for the most part, have been unclear, understudied, and secretive.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.035 | 0.019 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".