APPLYING THE RAPID MANAGEMENT ASSESSMENT (RMA) METHODOLOGY TO ECOSYSTEM MANAGEMENT.
Bibliographic record
Abstract
In the fall of 2002, approximately sixty undergraduate students from a broad range of disciplines at Acadia University enrolled in an interdisciplinary course entitled Sustainable Nova Scotia. While the theoretical and practical implications of this pedagogical approach are to be discussed elsewhere, this paper focuses on one course project that tested the feasibility of the Rapid Management Assessment (RMA) process developed by the Protected Areas Conservation Trust of Belize, Central America to hone interdisciplinary analysis in sustainable resource and environmental management. This project also tested the practicality of adapting the RMA process designed for protected area applications in a developing country to application in a predominantly working landscape in a developed country. The methodology included the minor revision of the RMA procedures manual to fit a Canadian working landscape, and the facilitation of eight upper-level students in an interdisciplinary team of student scientists/ecosystem managers. The disciplines represented included economics, business administration, environmental science, political science, recreation management, biology and arts. This team was charged with advising on sustainability strategies for the Gaspereau/Black River Watershed in Kings County, Nova Scotia. This project included a
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 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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".