Bibliographic record
Abstract
Forecasts made in planning policy are rarely achieved in the practicalities of local application, and the case for landscape conservation is no exception. The critical divide between landscape policy developed by upper-tier government agencies and the implementation of those conservation measures at a local level is a phenomenon common to many locations. A specific case of this divide was studied in Ontario, Canada over a span of time between the passing and defeat of one planning act and the introduction of another. Through a series of interviews conducted with both the creators and the future implementers of the landscape policy in those acts, central issues that contribute to conservation resistance were examined. This qualitative study compares the responses, identifies the differences, and in the end suggests strategies that may be useful to other jurisdictions to help foster a better land-use planning environment for landscape interpretation, use, and protection in the development process. The concept of landscape: theory and application “Landscape” is an idea that has a long tradition in academic literature (Sauer, 1925; Hartshorne, 1939; Hoskins, 1969; Meinig, 1979; Cosgrove, 1984; Schama, 1995). Interest in the concept's utility for planning has grown in the last decade (Mitchell et al ., 1993; Maines and Bridger, 1992; Watson and Labelle, 1997; Cardinall and Day, 1998; Rydin, 1998; McGinnis et al ., 1999). It has been acknowledged that it can serve as a basis from which planners can integrate natural and cultural elements and issues – historically, two realms polarized from each other (Olwig, 1996).
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".