Assessing the Management Performance of Biodiversity Conservation Initiatives and Investments: an Institutional Approach
Bibliographic record
Abstract
"An institutional analysis framework for assessing the management performance of biodiversity conservation investments and initiatives is developed in this chapter. While the focus is on biodiversity, the framework is also more widely applicable to problems involving the provision of public goods. This institutional approach is useful for a number of reasons. First, it uses capital assets to describe the state-of-the-world and specify the full spectrum of resource flows available to resource users. This allows analysts to frame the goals and objectives of various actors in terms of the attributes and characteristics of natural, manufactured, human, social and economic capital, and facilitate transparent discussions regarding sustainability. Second, this approach explicitly integrates the Institutional Analysis and Development (IAD) framework with the results-based management approach increasingly being used in the private and public sectors. The IAD framework provides a system for classifying rules and norms according to their function and to incorporate them into the action-activity-output-outcome-impact hierarchies used for results-based management. Third, the integrated institutional framework can be applied at different levels of analysis--political, policy implementation, and operational--in such a way that stimulates thoughtful policy design, analyses, and monitoring of biodiversity conservation investments and initiatives. Taking a multi-level perspective to policy design and implementation is necessary if we are to understand the dynamics of adaptive management, develop effective and efficient conservation investments and initiatives, and account for the full range of direct and indirect benefits of ecological research activities."
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 | 0.045 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".