Institutional investors and shareholder engagement: The Fonds Desjardins
Bibliographic record
Abstract
Following in the footsteps of the celebrated California Public Employees’ Retirement System (CalPERS), more and more institutional investors are developing policies governing their proxy voting rights at annual general meetings to clearly express shareholders’ interest in environmental, social, and corporate governance issues. They are also increasingly numerous in promoting responsible investment practices through these policies. The object of this study is to examine the extent to which votes cast by the Fonds Desjardins, a major Canadian institutional investor, at the annual general meetings of firms in which it invests comply with its proxy voting rights policy and its public commitment to the social responsibility of these firms. The analyses were based on the votes recorded on the Fonds Desjardins website from July 1, 2018, to June 30, 2019. Of the 168 votes analysed, 35 did not comply with the Fonds’ policy, reflecting a non-compliance rate of 20.8%. The analyses show that votes on environmental issues are the most diverged from the institution’s policy during the period under study. Overall, the results indicate that the votes cast by the Fonds Desjardins at annual general meetings do not always correspond to the Fonds’ proxy voting rights policy. These findings raise questions about the real motivation behind such policies. Are they a genuine or a symbolic tool?
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".