What matters for socially responsible investment (SRI) in the natural resources sectors? SRI mutual funds and forestry in North America
Bibliographic record
Abstract
Socially responsible investment mutual funds have played an active role in encouraging sustainability in the natural resources sectors, particularly in North America's forest industry which tends to be reactive in adopting sustainable practices. A survey of socially responsible investment mutual funds in Canada and the US was first undertaken in 2006 and then replicated in 2010--11 to understand the implications of this growing investment practice on the natural resources sector, with a focus on forestry. While we did not expect to find a convergence in environmental, social or governance criteria among funds, this study found that environmental criteria are most important to respondents in evaluating natural resource stocks, and that this is stable over time and consistent according to fund size. Governance criteria became prominent in 2010--11, perhaps a result of the Global Financial Crisis. These results build on literature examining the investment evaluation process for socially responsible mutual funds. What the findings highlight is that evaluation criteria are dynamic, responding to changing attitudes and firms should consider this in developing their sustainability agenda. Some socially responsible mutual funds have played a unique role in the forest sector, working collectively with Non-Government Organisations and civil actors to influence forest companies to improve sustainability, and have divested shares in forest companies that do not comply with their demands. Our results improve understanding for what is important to socially responsible mutual funds in evaluating the forest sector. The study shows a decline in importance for forest certification over the period and that the Forest Stewardship Council scheme is viewed as most credible by respondents. However, poor financial returns in the forest sector may constrain further attention from socially responsible mutual funds.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".