Multi-Party Monitoring in Ontario: Challenges and Emerging Solutions
Bibliographic record
Abstract
The demand for environmental monitoring information capable of informing decision-making in environmental management is growing at a rate faster than can be provided by traditional governmental sources. Non-governmental organizations and academics are increasingly becoming involved in the collection and analysis of environmental information to fulfill the need. However, these data are rarely used to inform decision-making. Multi-party monitoring has the potential to address this disconnect. Ontario has a wide range of environmental monitoring programs that embrace the public at various levels of involvement, from local community initiatives to provincial and federal programs. Only recently has there been a collective process to bring participants from these various programs together in discussions. This process was led by new, primarily non-government monitoring actors who were instrumental in launching a new coordinating body, the Ontario Ecosystems Monitoring Council. This paper draws from experiences gained from this process and discusses: (1) the challenges of multi-party monitoring; and, (2) solutions that have been emerging in Ontario. The three main challenges centre on: science, including issues of monitoring protocol standardization and monitoring within the context of an ecosystem framework; community engagement and leadership including issues of networking and capacity building; and governance including issues of collaboration and use of monitoring data by decision-makers. Solutions to these challenges are emerging and a number of these at various scales are highlighted. La demande pour une information de surveillance qui soit en mesure d’eclairer la prise de decision en matiere de gestion environnementale croit a un rythme plus rapide que ce que ne peuvent fournir les sources gouvernementales traditionnelles. Les organismes non gouvernementaux et les universitaires sont de plus en plus appeles a participer a la cueillette et a l’analyse de l’information de nature environnementale pour repondre a ce besoin. Toutefois, ces donnees sont rarement utilisees pour eclairer la prise de decision. C’est pourquoi la surveillance multipartite pourrait permettre de rectifier cette situation. L’Ontario est dotee d’une vaste gamme de programmes de surveillance environnementale qui font participer le public a differents niveaux, des initiatives communautaires locales aux programmes provinciaux et federaux. Ce n’est que recemment qu’on a vu naitre un processus collectif visant a amener les participants de ces divers programmes a discuter ensemble. Ce processus etait mene par de nouveaux acteurs en matiere de surveillance, pour la plupart non gouvernementaux, qui ont grandement contribue a lancer un nouvel organisme de coordination, l’Ontario Ecosystems Monitoring Council. Cet article decoule des experiences acquises au cours de ce processus et presente un examen des defis de la surveillance multipartite et des solutions qui ont vu le jour en Ontario. Les trois principaux defis sont l’aspect scientifique, notamment les questions de normalisation du protocole de surveillance et de surveillance dans le cadre d’un ecosysteme; l’engagement et le leadership de la collectivite, notamment les questions de reseautage et de developpement des ressources; et la gouvernance, notamment les questions de collaboration et d’utilisation des donnees de surveillance par les decideurs. Des solutions a ces defis commencent a voir le jour; on fait ressortir dans cet article un certain nombre d’entre elles, a differentes echelles.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".