La mesure d'impact et l'Investissement Socialement Responsable (ISR): Un tour d'horizon (Impact Measurement and Socially Responsible Investing (SRI): An Overview)
Bibliographic record
Abstract
French abstract: Apres plusieurs dizaines d’annees de travaux, la recherche academique sur l’information non financiere a desormais atteint un certain niveau de maturite. La litterature a neanmoins eu tendance a s’interesser essentiellement aux liens entre performance extra-financiere et performance financiere, au detriment de la performance extra-financiere en tant que telle. C’est dans ce cadre qu’un nouveau champ de recherche est recemment apparu autour de la question suivante : « quels sont les impacts extra-financiers des investissements socialement responsables (ISR) ? ». Dans un contexte de developpement rapide de l’ISR et de l’interet croissant pour la mesure d’impact, l’objectif de cet article est de mieux comprendre les specificites et les enjeux de la mesure d’impact, principalement pour les investisseurs. Apres avoir trace les contours de la montee en puissance de l’ISR, nous analysons en detail trois problematiques majeures pour la mesure d’impact : 1) la mesure de la performance extra-financiere, 2) la distinction entre mesure de performance et mesure d’impact et 3) l’agregation de mesures d’impact au niveau d’un fonds. Afin de guider les recherches ulterieures et base sur cette analyse, nous offrons egalement un cadre conceptuel qui permet d’identifier les enjeux de la mesure d’impact a chaque etape du processus de construction et de communication de l’information non financiere. English abstract: After several decades of work, academic research on non-financial information has now reached maturity. Yet, much of the literature has tended to focus on the relationship between financial and extra financial performance only. In order to better understand non-financial performance itself, a new research field has recently emerged on the topic of the non-financial impacts (rather than performance) of Socially Responsible Investment (SRI). Faced with the fast development of SRI and the growing interest for the notion of impact, the goal of this article is to provide a better understanding of the specificities and challenges of impact measurement, particularly for investors. We first explain the different stages of the SRI movement. Then, we discuss three key major issues for measuring impact: 1) measuring non-financial performance; 2) distinguishing between impact assessment and performance measurement and 3) aggregating impact indicators at the level of a fund. Based on this analysis, we offer a conceptual framework designed around the challenges of impact measurement at each step of the process of construction and communication of non-financial information. We expect this framework to help further research on the topic.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".