Towards Plural Values in Environmental Decision-Making: The Case of Lake Saint-Pierre's Environmental Assessment
Bibliographic record
Abstract
Coastal and marine ecosystems are under increasing anthropogenic pressure. This pressure is closely related to a too narrow definition of nature’s values in decision-making processes. Assessing plural values that encompass not only economic factors, but a variety of different value dimensions, such as socio-cultural and intrinsic values, is imperative for sustainable ecosystem management. Nevertheless, the assessment and consideration of multiple values is still scarce. To promote a perspective of plural values in environmental decision-making, we aim to integrate qualitative aspects into the framework of the decision support tool Life Cycle Analysis (LCA). To this end, we assessed how the multiple values that people attribute to ecosystems change when they experience ES loss. We conducted semi-structured interviews with local stakeholders along a coastal to freshwater gradient in two case study areas along the St.-Lawrence River delta in Southern Quebec, Canada. \n\nWe orient our analysis along the conceptual framework of multiple value dimensions of the IPBES, which differentiates between intrinsic, instrumental, and relational values. This qualitative assessment of plural values is a first step towards the construction of a set of novel indicators in LCA to mainstream the ES concept in decision-making processes. Our research is embedded in the international research project Cost to Coast [C2C] that brings together social science (qualitative assessment of plural values) and natural science approaches (biophysical modeling of ES loss). Our preliminary results suggest a strong sense of place, landscape aesthetics, and environmental justice as main factors that positively influence ES values. These factors need to be emphasized in policymaking to assure an integrative management of coastal and marine areas.
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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.027 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".