Enhancing natural habitats conservation success on private lands by understanding and addressing deficiencies in social capital
Bibliographic record
Abstract
Over the last decades, biodiversity conservation in Quebec province, Canada, has evolved from an ecology-centered discipline, mostly focused on ecosystem protection on public lands (e.g., protected areas and reserves), to an interdisciplinary field with increasing participatory efforts deployed to preserve private lands' habitats. Yet, conservation on private lands has experienced mixed results, notably because of limits regarding the creation of positive relationships to support community capacity to conserve through landowner engagement. It has proven difficult to integrate social knowledge and realities in the governance of conservation and to act on social-ecological issues to reach conservation objectives. In this paper, we study how social relationships shape conservation governance, and we illustrate the opportunities that these relationships offer and the limits that they impose. To do so, we explore social representations of social issues in conservation as perceived by actors and expressed during three participative workshops, carried out with a diverse array of actors in 2019 and 2020. We analyze our data through the concept of social capital to depict relationships through four dimensions (structural, functional, relational, cognitive). We find that social capital that supports landowner engagement and community capacity to conserve suffers from four forms of deficiencies. These are negative social capital, diverging social representations, dissociated social capital dimensions and sub-optimized social capital. These deficiencies are most prominent in relationships that link local actors to the provincial government, followed by relationships between municipal actors and other regional actors. We argue that bracing social capital through efforts that tackle all four dimensions at once might be the key to enhancing social connectivity in conservation governance. We conclude by identifying three broad needs for bracing social capital to improve conservation governance on private lands. These are 1) growing a shared understanding of conservation challenges through improved communication venues, 2) establishing feedback systems between local actors and decision-makers to encourage reciprocity in relationships, and 3) developing systemic knowledge to better inform conservation actions and avoid false solutions.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".