Disentangling ‘ecosystem services’ and ‘nature’s contributions to people’
Bibliographic record
Abstract
People depend on functioning ecosystems, which provide benefits that support human existence and wellbeing. The relationship between people and nature has been experienced and conceptualized in multiple ways. Recently, ecosystem services (ES) concepts have permeated science, government policies, multi-national environmental agreements, and science–policy interfaces. In 2017, the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) introduced a new and closely related concept – Nature’s Contributions to People (NCP). The introduction of NCP has sparked some lively discussion and confusion about the distinguishing characteristics between ES and NCP. In order to clarify their conceptual relation, we identify eleven specific claims about novel elements from the latest NCP literature and analyze how far ES research has already contributed to these corresponding conceptual claims in the existing ES literature. We find a mixed-picture, where on six specific conceptual claims (culture, social sciences and humanities, indigenous and local knowledge, negative contributions of nature, generalizing perspective, non-instrumental values and valuation) NCP does not differ greatly from past ES research, but we also find five conceptual claims (diverse worldviews, context-specific perspective, relational values, fuzzy and fluid reporting categories and groups, inclusive language and framing) where NCP provides novel conceptualizations of people and nature relations.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.009 | 0.022 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".