The use and abuse of ecosystem service concepts and terms
Bibliographic record
Abstract
The language used in ecology and conservation shapes our understanding of and actions toward the natural world. Among the most commonly used terms, ‘ecosystem services’ has become central to conservation discourse, defined as the benefits humans derive from ecosystems. While the ‘ecosystem services’ framework has effectively communicated nature's contributions to human well-being, its anthropocentric focus raises concerns about conceptual accuracy and ethical implications. By prioritizing human utility, the term risks misrepresenting ecological roles and marginalizing conservation efforts for species and ecosystems without immediate economic value. Additionally, its misuse in research and policy has led to confusion by conflating ecological processes with human-centered benefits. Although Nature's Contributions to People (NCP) has been proposed as an alternative framework to address these concerns, it faces similar limitations. Rather than replacing one anthropocentric framework with another, we argue for a broader adoption of the already existing ‘ecosystem functions’ framework. This approach provides a more accurate descriptor of ecological processes while avoiding the conceptual and ethical pitfalls of reducing ecosystems to their benefits for humans. Thus, the ‘ecosystem functions’ framework offers a step toward a more holistic and inclusive approach that respects the complexity of ecological relationships and supports effective conservation practices.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".