Canadian Pharmaceutical Industry in Global Value Chains: Influence of Dominant Trading Partner
Bibliographic record
Abstract
Participation of a country in global value chains is highly dependent on types of interaction with its major trading partners. The purpose of current research is to uncover the US influence on the Canadian pharmaceutical industry insertion into global value chains. The first part of the analysis is devoted to key trends of the US and Canadian pharmaceutical industries development under the expansion of global value chains. As a result, the following hypotheses are investigated: (1) Canada’s participation in pharmaceutical value chains is mostly regional other than global; (2) Canada’s regional value added is primarily generated through cooperation with the US pharmaceutical sector; (3) dominant positions of the US corporations on the world as well as Canadian pharmaceutical markets stifle Canada’s integration into global value chains. The second part of the research describes the quantitative approach to the hypotheses testing. For instance, data from the World Input-Output Database is used to calculate the origin of value added based on the geography and product type (national and foreign value added in the exports of final and intermediate goods). The final part of the paper deals with the data interpretation and contains conclusions. Namely, it was found that pharmaceutical GVCs in North America are in fact regional for most countries and Canada is not the exception (first hypothesis proved). Further, foreign value added content of Canadian pharmaceutical exports is primarily generated in the US (second hypothesis proved). At the same time, the last hypothesis has not gained support in current research. The share of foreign value added growth during the period of 2002–2014. Thus, it can be stated that Canada has a positive experience of integration into GVCs under dominant trading partner.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".