Building on Lessons From Landscape-Level Integrated Assessment to Inform Key Elements in ILM
Bibliographic record
Abstract
Integrated landscape management (ILM) is gaining increasing attention among Canadian practitioners for its capabilities to address environmental, social and economic goals simultaneously, while promoting sustainable development. ILM is relatively new in the family of integrated assessment (IA) approaches. This paper focuses on the lessons learned from the longer history of IA and its spatially explicit applications to inform current applications of ILM in Canada. We specifically focus on the role of spatially explicit information in addressing critical issues already flagged by ILM practitioners. In particular, we focus on challenges in defining roles and benefits of participation, linking environmental and social issues in integrated models and, testing and presenting uncertainties in the integrated models. We illustrate how some of these challenges were addressed using case studies from Canada, the United States and Europe. The experiences from these studies show that using GIS and other geospatial information tools can be effectively integrated into IA to enhance the relevance of IA for decision-making and to assist with participatory engagement activities by introducing new ways to explore and present choices and options to practitioners. The use of geospatial tools also enable integrated modelling to occur at spatially disaggregated levels, which increases the context under which external influences may be considered. Despite these benefits, current applications of these tools tend to be limited to technical representation of system conditions and the visualization of biophysical processes. A deficiency of socio-economic indicators and change information, including policy and management actions and impacts, means that outcomes from these tools currently have limited relevance for policy-makers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.017 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".