People working with nature: a theoretical perspective on the co-production of Nature’s Contributions to People
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The co-production of Nature’s Contributions to People (NCP) is a set of processes in which anthropogenic inputs (i.e. material or non-material actions and the assets supporting these actions) and natural inputs (i.e. ecological structures and processes) interact to produce NCP. An interdisciplinary understanding of NCP co-production can support decision-making on ecosystem management or NCP use, given natural constraints, limited human inputs, possible adverse effects and trade-offs arising from co-production. In this paper, we show that mechanisms of co-production at the ecosystem level and the NCP flow level are fundamentally different. At the level of ecosystems, people manage natural structures and processes to influence the production of potential NCP (e.g. via planting, restoring, fertilizing). At this level, anthropogenic inputs can partially substitute for natural inputs, but natural inputs are necessary whereas anthropogenic inputs are not. At the level of flows, co-production actions convert potential NCP into realized NCP and quality of life (e.g. via harvesting, transporting, transforming, consuming, and appreciating NCP). At this level, anthropogenic inputs are complementary to natural inputs, although some substitutability can occur at the margin. Analysing the substitutability and complementarity between natural and anthropogenic capitals, as well as the adverse effects or mutual enhancement between them, is crucial for informed decision-making on landscape and NCP management. This understanding enables the identification of strategies that can ensure NCP supply and increase human well-being in a sustainable manner.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it