Environmental policy development and decision-making: \nA scenarios and systems mapping approach to large-scale systems re-design
Bibliographic record
Abstract
Taking Stock was a year-long project undertaken by Alberta Eco-Trust to understand and address long- standing issues with respect to the way Albertans create and use environmental policy to make decisions concerning land, air, water and bio-diversity in the Province. This paper describes and critiques the methodology used in the project: Scenario Development, Systems Mapping and a form of Systems Design. These three approaches, used in combination, represent a substantive interdisciplinary approach to a complex multi-stakeholder problem area. General findings conclude that engaging ‘seasoned policy practitioners’ with widely different perspectives in all work phases has promising potential but comes with a number of additional ‘care points’. The aspirational form of the scenarios proved to be a useful format for participant understanding but required ‘expertise’ in creating the necessary narratives and articulation of the challenges. The systems mapping format brought to life the complexity and structure of the current dynamics but required sufficient learning time to ‘read the maps’. The representational form of the systems maps proved to be a useful (but restricted) format for the design phase with the systems requirements generated by the scenarios work providing the ‘design criteria’.
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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".