Bibliographic record
Abstract
This thesis is about "landbanking". The case study of Red Deer was chosen to illuminate the concept rather than vice versa. The case of Red Deer is interesting in itself but has a wider importance because the Federal Government in Canada and several Provincial Governments have recently pledged massive financial support of local "landbanks". This study is not a micro-empirical study of a particular land market but is concerned with a certain policy and its market implications. The focus is on landbanking as a system of urban land conversion in which government agencies play a direct and active role instead of a passive regulatory role. Landbanking has been a particularly confused and contentious topic, the first three chapters of this thesis attempt to clear away this confusion by an analysis of the concept and the relevant literature. Several distinct "schools of thought" on landbanking are identified and several erroneous conceptions are refuted. The economics of landbanking and particularly those issues relevant to a cost-benefit analysis are examined in depth in the methodology chapter. The next three chapters are a detailed case study of the Red Deer landbank from its inception to December 31, 1972. The legal framework of its operation, the administration of the program, the financial history of the program, and the policies that structured the landbank are examined. Example subdivisions are analysed. The final chapter, is a cost-benefit evaluation of the landbank which relies on the conceptual base established in the earlier chapters and the empirical data gathered in the case study.
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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 | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".