Abbreviations and acronyms
Bibliographic record
Abstract
This FDI Qualities Review of Canada provides an assessment of how foreign direct investment (FDI) contributes to Canada's sustainable development.The review uses OECD and non-OECD data sources and draws on the qualitative insights from an OECD business consultation on the corporate sustainability practices of a group of domestic and foreign companies operating in Canada.It provides initial policy considerations to improve the impact of FDI on inclusive and sustainable growth in Canada.The report comprises five chapters.Chapter 1 provides an overview of the main challenges for sustainable development in Canada, analyses recent FDI trends, and presents a summary of the main findings of the study, which show the role that FDI currently plays in supporting sustainable development.Chapter 2 examines the impact of FDI on trade and GVC integration, productivity and innovation.Chapter 3 analyses the impact of FDI on employment creation, job quality, and skill development.Chapter 4 assesses how FDI influences diversity and inclusion of vulnerable workers (women, indigenous peoples, foreign workers from disadvantaged backgrounds, and people with disabilities) in the labour market.Finally, Chapter 5 provides an evaluation of how FDI contributes to Canada's net-zero transition.The review has been prepared by the OECD in close co-ordination with Invest in Canada.It is part of a series of FDI Qualities Reviews, supporting the implementation of the OECD Council Recommendation on FDI Qualities for Sustainable Development, adopted by OECD Ministers in 2022.The FDI Qualities Reviews, conducted so far in Ireland (2021), Jordan (2022), Portugal (2022), Slovak Republic (2022), Austria (2023), Chile (2023), and Croatia (2023), shed light on how FDI contributes to a country's sustainable development priorities.They help identify areas where such impact can be improved and provide tailored policy advice.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.106 | 0.062 |
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".