Ecosystem services decision support tools: exploring the implementation gap in Canada
Bibliographic record
Abstract
This paper explores the degree to which the ecosystem services (ES) concept and related tools have been integrated and implemented within the Canadian government context at both the provincial/territorial and federal levels. The research goals of the study were to qualitatively assess the extent to which ES assessment is being integrated at different levels of government, consider the barriers to implementation, and draw lessons from the development and use of Canada’s Ecosystem Services Toolkit: Completing and Using Ecosystem Service Assessment for Decision-Making—An Interdisciplinary Toolkit for Managers and Analysts (2017), jointly developed by a federal, provincial, and territorial government task force. Primary data were collected through targeted semi-structured interviews with key informants combined with a content analysis of ES-related documentation from government websites. Results indicate that while the term ES is found in documentation across different levels of government, there appears to be an ES implementation gap. Issues of conceptual understanding, path dependency, a lack of regulatory mandate, lost staff expertise, and competition with overlapping conceptual approaches were identified as barriers to ES uptake. Areas requiring further policy and research attention are identified.
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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 | 0.043 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".