Lost in Translation: A study of how unclear application to the Investment Canada Act has deterred foreign investment into the oil sands
Bibliographic record
Abstract
Prime Minister Harper’s statement following the 2012 takeover of Nexen by CNOOC that “going forward, the Minister will find the acquisition of control of a Canadian oil-sands business by a foreign state-owned enterprise to be of net benefit, only in an exceptional circumstance” represented a major shift in the Canadian government’s attitude towards foreign direct investment into the energy sector by state owned enterprises. While the policy announcement was intended to clarify application of the Investment Canada Act (ICA), it has done the opposite. By adding the term “exceptional net benefits” when reviewing state owned enterprise’s bids for control of Canadian oil sands companies, the Canadian government has unfairly targeted Asian SOEs and confused the use of the net benefits test when assessing foreign investments. The resulting policy has had a negative effect on the development of the oil sands and on investor sentiment towards Canadian energy projects. Evidence suggests that the policy change has had negative consequences on investment activity including: increasing investment costs for acquirers, increasing costs of capital and decreasing available capital for junior oil and gas companies, and creating uncertainty in the minds of foreign investors leading to them questioning Canada as a place for future investment.
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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 | 0.009 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".