Social Ethics of Landscape Change
Bibliographic record
Abstract
Understanding stakeholder values is crucial to the development of a community-based model of landscape change. Be that as it may, engagement techniques are still in their infancies, and land-use planners are struggling for tools to facilitate discourse on public values related to landscape change. Accordingly, this article responds to urgent needs to define planning processes that represent the values of stakeholders, empower communities, and lead to landscape changes that maintain and enhance a community's sense of place. It does so by exploring the combination of photo elicitation and narrative as a form of civic science aimed at engaging citizens in the planning process. Findings from a study incorporating these techniques are used to show the merits of this participatory form of inquiry. The authors argue the use of stories, unlike traditional public engagement techniques, allows the landscape-change process to be situated within the social meanings relevant to a community.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.064 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".