Fostering the Provisioning of Ecosystem Services by Private Landowners
Bibliographic record
Abstract
The past decade has witnessed a burgeoning interest within scholarly and applied circles in the re-casting of environmental amenities as commodities for trade, marketable in much the same way as a loaf of bread or a quart of strawberries. With the ostensibly growing foothold of the 'ecosystem services' (ES) paradigm, the public good nature of environmental stewardship has been thrust into the limelight. The newly-emergent perspective holds thus: given that individual landowners are expected to bear the responsibility of meeting heightened standards of environmental protection through additional expenditures or foregone development opportunities, and yet society at large reaps the benefits, they should be remunerated by society. This thesis explores the governance arrangements that would serve to foster the provisioning of ES by private landowners. A heuristic framework is first developed, offering a means of systematically contemplating critical issues influencing the viability and performance of ES governance alternatives. Set in eastern Ontario, the empirical portion of the research assesses the interests of landowners, and program and policy professionals, for different ES governance mechanisms. In brief, interests were varied, with an openness to a range of arrangements. Notably, preferences tended toward arrangements exhibiting cooperative and collaborative leanings, and away from those with competitive underpinnings. These understandings inform the elaboration of a set of high-order design features envisioned as preconditions in a governance 'architecture' supportive of the provisioning of ES. The findings suggest that a more open embrace of hybridity in institutional arrangements may offer a way forward as ES governance alternatives continue to be conceived. They also point to the need for a re-imagining and re-constituting of relationships such that they truly embrace the principles of mutual regard, reciprocity, and trust; such ‘relations of regard’ may serve to realize a renewed social contract between those working the land, and those looking on from beyond the farm (or woodlot) gate. Consistent with this suggestion, the findings underscore the need for a greater sensibility to the diverse motivations that inspire the provisioning of ES. In contemplating prospects for reflexive governance approaches to enhance the provisioning of ES, the findings suggest reason for cautious optimism.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| 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".