Off-stage ecosystem service burdens: A blind spot for global sustainability
Bibliographic record
Abstract
The connected nature of social-ecological systems has never been more apparent than in today's globalized world. The ecosystem service framework and associated ecosystem assessments aim to better inform the science–policy response to sustainability challenges. Such assessments, however, often overlook distant, diffuse and delayed impacts that are critical for global sustainability. Ecosystem-services science must better recognise the off-stage impacts on biodiversity and ecosystem services of place-based ecosystem management, which we term 'ecosystem service burdens'. These are particularly important since they are often negative, and have a potentially significant effect on ecosystem management decisions. Ecosystem-services research can better recognise these off-stage burdens through integration with other analytical approaches, such as life cycle analysis and risk-based approaches that better account for the uncertainties involved. We argue that off-stage ecosystem service burdens should be incorporated in ecosystem assessments such as those led by the Intergovernmental Platform on Biodiversity and Ecosystem Services and the Intergovernmental Panel on Climate Change. Taking better account of these off-stage burdens is essential to achieve a more comprehensive understanding of cross-scale interactions, a pre-requisite for any sustainability transition.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.007 | 0.022 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".