Scenario planning as a method for imagining rural municipal futures
Bibliographic record
Abstract
In 2014 the Alberta Centre for Sustainable Rural Communities (ACSRC) conducted research on municipal governance and land use planning in Alberta. The Land Use Framework (LUF) was created in 2008 and divided the province into 7 land use planning regions. The LUF regional plans were originally intended to be complete between 2010 and 2012, however, as of 2015 four of the seven regional plans had not even begun. As a part of this larger project, the ACSRC conducted two scenario planning workshops in Camrose, Alberta. These workshops brought together 50 municipal participants to engage in an innovative method of scenario planning to model desirable and undesirable outcomes for rural municipalities. This method was used to help municipalities to identify driving forces and uncertainties for rural municipalities in Alberta. By engaging with questions around governance, proximal and distal causation, unanticipated variables, and identifying trends and patterns allowed municipalities to develop a nuanced and innovative collective vision for rural municipal governance in the province. This presentation will discuss and assess the method used for our scenario planning workshops as a rural community development tool, as well as the results from these two workshops. Specifically, we present and examine the different dynamics and scenarios that have salience or traction with rural municipalities in the province, and focus on the tension between democratic and representative concerns on one hand, and administrative functionality and service provision on the other.
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 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".