Understanding the acceptance of market-based instruments for the ecosystem service of water quality
Bibliographic record
Abstract
Non point source (NPS) contamination in southern Alberta's agricultural belt is a known issue.While point source contamination is clearly regulated and managed the mandate and responsibility for dealing with NPS water quality issues appears unclear and overlapping.Market-based instruments (MBIs) are being promoted in Alberta as a tool to help meet environmental management goals.This paper explores the role legitimacy, accountability and fit of MBIs based on two sets of semi-structured interviews, conducted to provide the background, context and perceptions around MBIs for water quality.One set of interviews focused on experts in the area of developing, implementing or analyzing MBIs for environmental objectives.The second set of interviews focused on local subject matter experts and knowledgeable stakeholders in the case study areas in southern Alberta.Initial results indicate that while MBIs could have an important role in delivering better water quality outcomes, the issues of fit, accountability and legitimacy need to be addressed in the MBI design process.Currently these are not adequately addressed in the Alberta context MBIs are relatively new tools for environmental management.Q methodology will be employed to further expose the values orientations and perspectives.
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 | 0.023 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".