Disentangling ‘ecosystem services’ and ‘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
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.002 |
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