Socially Responsible Investing in ”High-Net-Worth” Asset Management Firms in Canada: An Exploratory Study
Bibliographic record
Abstract
Socially responsible investing (SRI) is an increasingly well-known investment strategy. However, in most nations, SRI is not mainstream practice. This paper investigates perceptions of SRI amongst investment professionals from “high-net-worth” investment firms in Toronto, Canada. Existing corporate practices in relation to stock selection and client relations are documented, in order to assess how these practices might facilitate or prevent SRI. Views of SRI, and its current and potential future role in investment practice, were also explored. Results suggest that, while awareness of SRI has increased in recent years, it has not become accepted practice in high-net-worth investment firms. This lack of adoption stems from the perceived additional burden of researching the ethical (and not just financial) performance of companies, rather than any fundamental disagreement with the principles of SRI. In addition, interview participants pointed to low levels of client demand. Increased awareness of SRI among both professionals and clients was seen as the most effective way of increasing its adoption. The paper concludes by discussing the implications of this research for social responsible investing in Canada’s high-net-worth investment firms as well as in the broader investment world.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".