Corporate Stewardship of Urban Green Space: A Sanofi Pasteur Case Study
Bibliographic record
Abstract
Corporations stewarding green spaces, termed corporate stewardship, is a new idea being implemented by Sanofi Pasteur, part of the French multinational pharmaceutical company Sanofi. In 2019, Sanofi Pasteur initiated a project to steward a section of the City of Toronto’s G. Ross Lord Park located in a northern Toronto ravine. There is essentially no information available on corporate stewardship to help guide plans, practices and partnerships, especially in urban centres. Thus, there is a pressing need to provide a framework to implement stewardship plans and to measure their success given the growing interest in private corporate stewardship such as Sanofi’s. The current paper develops a strategic stewardship plan for Sanofi Pasteur and sets out a new framework to help guide corporate stewardship success. A ‘criteria-and- indicators’ approach from urban forest management planning (Kenney et al. 2011) was adapted to identify key activities, timelines, and measure the progress of the work for the company to implement over the next three years (2022, 2023, 2024) in their 5-year stewardship commitment.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".