A need for assessing the resiliency of conservation funding
Bibliographic record
Abstract
The ability to address conservation challenges hinges, in part, on a robust understanding of complex social-ecological systems. Conservation funding is a critical component that can impede or facilitate our ability to understand issues and overcome conservation challenges. Conservation funding is dynamic and is often dependent on organizations sustained by individual contributions (e.g., memberships, donations). A shift in funding sources, away from federal government support, could lead to greater uncertainty and instability in conservation funding. Herein, we use an individual-based conservation organization database to demonstrate how to assess funding resiliency by identifying subpopulation typologies (subgroups of individuals) that reflect similar patterns in conservation contributions. We identified three typologies that provide North Dakota Game and Fish Department support for managing and protecting natural resources. Most (~68%) individuals (typology I) infrequently contributed to recreational fishing conservation; few (~9%) individuals (typology III) provided frequent contributions to recreational fishing conservation over the 11-year study period. While conservation funding has been relatively consistent for North Dakota Game and Fish Department, it may be subject to rapid change. Identifying the number of conservation typologies (e.g., diversity) and associated characteristics (e.g., frequency and amount of funding contributions, socio-demographic characteristics) could provide conservation-oriented organizations the ability to quantify, track, and predict underlying contribution trends that are masked by overall (i.e., population-level) funding patterns. Ultimately, identifying subpopulations and associated contribution patterns could aid in avoiding potential losses in conservation funding.
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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 | 0.045 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".