Privatization of Crown Corporations (CCs) and State-Owned Enterprises (SOEs) in Canada: goals and aspirations of government and participating businesses
Bibliographic record
Abstract
Privatization of Crown Corporations (CC) and State-Owned Enterprises (SOEs) has become an important worldwide phenomenon.Over the last few years, CCs and SOEs have been privatized in both developed and developing countries.In Canada, privatization emerged in the 1970s; however, it became fully operational as a federal policy in the mid 1980s when a number of both federal and provincially owned corporations were sold to private companies.From 1985 to 2005, federal government has collected close to $12 billion from the proceeds of privatization and more than $1.5 billion in lease revenues from airport authorities.There has been a lot of discussion as to whether privatization has succeeded in meeting the goals and aspirations of the policy initiative as well as of participating businesses.In evaluating the program, the original objectives of privatization have been achieved and the goals and aspirations of both government and participation businesses have been met.However, these were not without some challenges.Some recommendations and solutions to these challenges, and how to make privatization policy more effective, have been made in the study which the writer believes will enhance the policy initiative in future privatizations of CCs and SOEs.The study relied on exploratory, secondary, and primary sources data and information.Exploratory research took the form of casual discussions around privatization with professionals from the public and the private sectors to design a framework for the study.Secondary data was gained through a review existing literature.Primary data was mainly used to answer the research question by interviewing senior and middle managers of public-to-private companies, public institutions, and private companies were interviewed.It is hoped that the study will add to the existing knowledge on privatization of CCs and SOEs, as well as other forms that exist in public institutions, and local governments.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".