The Policy of Foreign Takeovers In Canada's Non-Renewable Resource Sector
Bibliographic record
Abstract
ABSTRACT The purpose of this thesis is to provide an overview and assessment of the policy and process related to foreign investments, and particularly either a partial or complete acquisition of companies operating in the natural resources sectors in Canada. More specifically, the purpose is to ascertain problematic features of that policy and process, and to identify and recommend some potential reforms for eliminating or at least minimizing those problems. For that purpose, this thesis provides an overview and analysis of two recent and precedent-setting takeover bids in the non-renewable resource sector that were reviewed by Prime Minister Stephen Harper’s Conservative government, namely the BHP Billiton ‘hostile’ takeover bid for Potash Corporation of Saskatchewan (PCS) in 2010, and the China National Offshore Oil Company (CNOOC) ‘non-hostile’ takeover bid of Nexen Inc. in 2012. The four central research questions are: Why did the federal government not approve the BHP Billiton takeover bid of PCS? Why did the federal government approve the CNOOC takeover bid of Nexen? What do these two decisions by the federal government suggest are the problems in the foreign investment review policy and process? What reforms are needed to the foreign investment review to eliminate or at least reduce some of those problems? The answers provided by this thesis to those questions are as follows. First, the federal government did not approve the BHP Billiton takeover bid of PCS because it was a ‘hostile’ bid strongly opposed publicly both by PCS and by the Saskatchewan provincial government. Second, it approved the CNOOC takeover bid of Nexen because it was a non-hostile bid and was not opposed either publicly or privately by Nexen or any provincial government. Third, both decisions by the federal government suggest that there are some significant problems with the foreign investment review policy and process. Fourth, several reforms are required to eliminate or reduce some of the significant problems with Canada’s foreign investment review policy and process that became evident in the context of the two reviews undertaken and decisions rendered by the federal Conservative government led by Prime Minister Stephen Harper.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".