The generations of cultural ecosystem services research
Bibliographic record
Abstract
Understanding the cultural dimensions of human-nature relationships and integrating them into decision-making is a central goal of conservation social science. One prominent avenue for this work is the characterization and analysis of cultural ecosystem services (CES) (i.e., nonmaterial aspects of the benefits derived from human-nature relationships). The Millennium Ecosystem Assessment introduced the term CES in 2005, and the ensuing decades have seen a blossoming of work on this topic-including extensive critique and the development of multiple closely related concepts. Because the need to recognize CESs (by whatever name) is not going away, we reflected on where CES research has been, where it is now, and where it might go. We refer to the current field as second-generation CES: a suite of approaches and innovations (biocultural indicators, relational values, and nonmaterial nature's contributions to people) that enhance, reject, or modify some of the premises of first-generation CES. These new approaches can be understood as a pluralistic menu of options to capture the essence of what CES aimed to, or failed to fully, represent. Nonmaterial factors (i.e., CES and conceptual offspring of CES) can affect conservation decision-making via 4 main channels: evaluation or assessment, elucidation of trade-offs, epistemic and social recognition, and, in some cases, the reclassification of what nature itself is.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".