Patriotism, place, and provisioning: assessing cultural ecosystem services through longitudinal and historical studies in Vietnam
Bibliographic record
Abstract
Cultural ecosystem services (CES) provide multiple benefits to people, including experiences, identities, and capabilities through both material and non-material means. There have been few studies of CES in Vietnam, despite a number of historical, religious, cultural, and customary traditions that have long influenced landscape values and management. We aim to identify a range of CES important to respondents in a study site in north-central Vietnam by providing a unique longitudinal view. Over a two-decade period, different ecosystem benefits have been obtained by local households, some of which have been influenced by cultural factors or could be considered CES. These have included material ecosystem services, including agricultural production, local medicinal plants, and culturally relevant craft materials. There are also non-material CES of interest, including those related to sense of place and national identities, spiritual and religious practices, and recreational and aesthetic benefits. However, over time there has been diminishing importance of some material resources as landscapes have changed from a mix of agricultural lands and natural forests to plantation forestry, and social impacts have resulted from increased labor migration, which has diminished sense of place among younger generations. Assessing these changes allows us to explore how CES are not static or pre-given but shift over time and within different contexts.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".