Soft path approach as a water management strategy: a case study in Thunder Bay, Ontario / by Allison Buonocore
Bibliographic record
Abstract
"The purpose of this research is to examine the criteria for a soft path approach as it may apply to municipal water management in the City of Thunder Bay. A categorization of Thunder Bay's water management practices was created to understand the city's current state. The soft path approach is a new strategy which aims to achieve sustainable water management by considering changes in social habits and practices as well as economic growth rates and structure. A framework of indicators was developed to evaluate the institutional capacity of a municipality to successfully implement the soft path approach. These indicators fit into six themes: technical, financial, institutional, social, political, and technological, and were applied to evaluate specifically the institutional capacity of Thunder Bay to implement the soft path approach. The methodology used in this research was semi-structured interviews of thirteen individuals from a variety of sectors in water management which included municipal and provincial employees, academics and private sector workers. It was found that Thunder Bay is well-suited to implement the soft path approach. There are strengths and weaknesses associated with each capacity which need to be addressed and this research provided recommendations for each capacity type. Three goals for Thunder Bay are provided and include: evaluating, understanding and looking at specific reasons for conservation and efficiency and then quantifying it; articulating a collective vision for a new water future through public engagement; and through the use of backcasting, Thunder Bay should be creating long-term water management strategies which set goals for certain targets such as "No New Water." Overall, this research proves that Thunder Bay is one area where the soft path can be implemented. Social, technical, institutional, financial, political and technological changes have to be made in order for this to happen, but there is a need for change and with participation and interest from local government and citizens, this change is achievable."-- from abstract.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".