Mesure d'Impact et Label ISR: Analyse et Recommendations (Impact Measurement and SRI Label: Analysis and Recommendations)
Bibliographic record
Abstract
French abstract: Ce rapport presente les travaux du Groupe de Travail Impact lance lors de la reunion du Comite du Label ISR du 1e decembre 2017, coordonnee par Nicolas Mottis pour le Comite Scientifique du Label ISR et qui a reuni une trentaine d’acteurs de la place financiere parisienne sur les questions de mesure d’impact des fonds labelises. Ce groupe a fonctionne de decembre 2017 a juin 2018 avec l’objectif de faire des propositions concretes au Comite du Label ISR, notamment sur l’evolution du referentiel utilise comme base de labellisation des fonds. Il a realise de nombreuses auditions d’acteurs francais et internationaux, une revue de litterature large sur la question de l’impact, une enquete ouverte par questionnaire et un appel a propositions. Cela a conduit a la formulation de 4 propositions visant a ameliorer la prise en compte des questions d’impact par le label. Ces propositions seront presentees au Comite du Label a l’automne 2018. English abstract: This report presents the work of the Impact Working Group launched at the meeting of the SRI Label Committee on December 1, 2017, coordinated by Nicolas Mottis for the Scientific Committee of the SRI Label and which brought together around thirty players from the Parisian financial center on questions of measuring the impact of labeled funds. This group operated from December 2017 to June 2018 with the objective of making concrete proposals to the SRI Label Committee, in particular on the evolution of the benchmark used as the basis for labeling funds. He has carried out numerous hearings with French and international actors, a broad literature review on the issue of impact, an open survey by questionnaire and a call for proposals. This led to the formulation of 4 proposals aimed at improving the taking into account of impact issues by the label. These proposals will be presented to the Label Committee in the fall of 2018.
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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 | 0.127 | 0.233 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.047 | 0.019 |
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".