Influences of Engaging in a Participatory Monitoring and Evaluation Process on Stakeholder Perceptions of Key Performance Indicators for Trails
Bibliographic record
Abstract
Trail use is growing globally. Managers confront the classic dilemma of protecting ecological integrity and providing enriching experiences. They concomitantly face the imperative for sustainability—contemporarily characterized by complexity, uncertainty, conflict, and change. Heightened levels of visitation are cause for immense concerns due to adverse impacts to the environment as well as visitor experiences. COVID-19 exacerbates these challenges as heightened levels of visitation are occurring, while managers simultaneously face decreases in conservation funding, and restrictions on protected area operations. Participatory monitoring and evaluation (PM&E) is an emerging in- novation to collaboratively address social-ecological challenges, such as issues as- sociated with trail use. This research is concerned with exploring the influences of engaging in a PM&E process on stakeholder perceptions of key performance indicators (KPIs) for trails. This study compares stakeholder perceptions of KPIs for trails before and after a PM&E workshop at the Niagara Glen Nature Reserve in Ontario, Canada. Results show that PM&E can facilitate consensus among stakeholders regarding the overall goals of management and associated KPIs for environmental management planning. Stakeholders were shown to experience a real change in their perceptions of KPIs. The PM&E process studied show that participants became more conscious of the wider social realities as well as their perceptions of trail management. The study has important implications for managers concerned with trails and sustainability, including building consensus among key stakeholders to reach management goals, enhancing localized decision making, and building capacity for management towards sustainability. Trails, as well as the wider community can ultimately benefit from participatory approaches to environmental management. Consensus-building through PM&E works to enhance decisions that account for a diversity of perspectives. Stakeholder participation in trail management increases the likelihood that local needs and priorities are met, while allowing stakeholders to build capacity and learn to effectively manage their environments. Furthermore, positive perceptions from being meaningfully involved in PM&E can ensure the support of constituents, which is imperative for the long-term success of management planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".