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Record W1814793474 · doi:10.1080/20430795.2012.690724

What matters for socially responsible investment (SRI) in the natural resources sectors? SRI mutual funds and forestry in North America

2012· article· en· W1814793474 on OpenAlexaffabout
William Nikolakis, David H. Cohen, Harry W. Nelson

Bibliographic record

VenueJournal of Sustainable Finance and Investment · 2012
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNatural resourceInvestment (military)BusinessCorporate governanceSustainabilitySocially responsible investingFund of fundsInstitutional investorSustainable developmentFinanceNatural resource economicsEconomicsPolitical scienceEcology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0050.004
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.008
GPT teacher head0.212
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations37
Published2012
Admission routes2
Has abstractyes

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