Carleton Centre for Community Innovation Measuring the Impact of Engagement in Canada
Bibliographic record
Abstract
Institutional investors are becoming more concerned with the environmental, social and governance (ESG) standards of companies in which they invest. For these investors companylevel ESG factors represent future risk when they hold their investments over a long period of time. Given the long-term nature of their portfolios, these investors engage with companies to raise these standards. This paper argues that corporate engagement has the potential to produce a positive change in company behaviour. It asks what leads to successful outcomes in engagement? We seek to quantify any observed positive change in corporate ESG standards that result from engagement. The paper extends the literature on stakeholder engagement. We use three case studies of engagements between institutional investors and companies in Canada over the past five years. We examine the outcomes of each engagement from the perspectives of the investor. We also consider the short term impacts and long term changes in corporate behaviour that resulted from engagement. Key Words: corporate engagement, shareholder engagement, stakeholder theory, environmental, social and governance ESG standards. Acknowledgements: We would like to thank our three case studies Canada Pension Plan
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".