Implications of U. S. MNCs for Canada and its Economic Policies in the Twentieth Century
Bibliographic record
Abstract
This study examines the U.S. Multinational Corporations implications and Canadian economic policies during 20th century. It discusses how the U.S MNCs put a heavy drain on the Canadian economy and what the Canadian government took different steps to counter them. The Canadian economy is heavily dominated by multinational corporations, which are nearly concentrated in all the sectors of its economy. This ‘undue’ foreign control caused a lot of balance of payments problem and developed a truncated industry in Canada. To overcome these problems the government appointed different commissions which submitted their recommendations to increase the national employment levels, diversity of employment, technological advancement, competitive strength, export volumes and reduce imports. In the light of these recommendations, the government adopted different policies to achieve its national economic goals. It gave taxation concessions to the local periodicals and publications to make them competitive with the U.S MNCs. Trudeau government announced National Energy Program (NWP) which put ownership restriction on the foreign MNCs and put them at disadvantageous position compared to Canadian MNCs. The Canadian government also introduced FIRA (Foreign Investment Review Agency) which was a screening body for the foreign investment and which caused a lot of resentment among foreign investors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.029 | 0.007 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".