Planning and well-being: Aesthetic perceptions in a deindustrializing landscape
Bibliographic record
Abstract
Halifax has experienced an uneven landscape of deindustrialization since the late 1970's. Theformer city of Dartmouth, now a planning region within greater Halifax, is an area which has remained quite industrial relative to the Halifax peninsula. The Imperial Oil refinery was a part of this remnant industrial landscape. Situated in the neighbourhood of South Woodside, the refinery has been a prominent feature on the waterfront skyline for almost a century. It is understood, appreciated, and despised differently according to different actors and observers— creating both stigmatization, wonder. The impacts of its presence are similarly dispersed. The refinery closure was met with sadness, ambivalence, but also, quite a bit of relief. The dynamics between lived experiences, and the broader global context and forces, shape future possibilities for community development, and there is a the tendency for poor engagement among different acting bodies—a function of an entrepreneurial mode of development. The poor coordination of planning efforts, and imbalanced power in decision making has resulted in a disengaged community. If justice were to be restored in land use decisions, there is potential to both restore well-being in a stigmatized community and take advantage of synergies between community capacity building and industrial development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".