Assessing community capacity for ecosystem management : Clayoquot Sound and Redberry Lake biosphere reserves
Bibliographic record
Abstract
Biosphere reserves are regions that are internationally recognized for their ecological significance and work towards ecosystem management.The concept of community capacity, as developed in the resource management and health promotion literatures, was applied to two such regions that were designated in 2000: Clayoquot Sound and Redberry Lake.The purpose of this comparative research was to better understand what constitutes the collective ability, or community capacity, these places have for fulfilling their functions as biosphere reserves.Community capacity is the collective mobilization of resources (ecological, economic/built, human and social capitals) for a specified goal.A mixed methods approach was taken.Self-assessments, both qualitative and quantitative, were used to determine community capacity in focus groups with biosphere reserve management, residents, and youth (grades 9-12).The results were compared to a statistics-based assessment of socioeconomic well-being.Semi-structured interviews for a related research project provided further insight.This comparative research made theoretical advancements by identifying key constituents of community capacity, including dimensions of the capitals and 'mobilizers,' or factors that motivate people to work for communal benefit.Mobilizers were found to be key drivers of the process of using and building community capacity.Four mobilizer categories were identified: the existence of, and changes to capital resources; individual traits; community consciousness; and, commitment.The practical implications of applying both qualitative and quantitative assessment methods were examined.It was found that there are several ways to conduct the socioeconomic assessment, and that adaptive methodological application is advised in research that attempts to be truly community-based-not just about, but for and with communities.It was found that, while it does not ensure a biosphere reserve's success, economic capital plays a key role in activating other resources beyond a time frame of three years, where social capital can be the primary driver for activity.Despite substantial differences politically, socially, and economically, both regions experienced similar challenges that can be largely attributed to a general lack of understanding of the biosphere reserve concept, and a lack of consistent, core funding.with the help of many people.My supervisor, Dr. Maureen Reed, proved to be more caring, insightful, and helpful during this academic journey than I could have ever hoped for.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".