Bibliographic record
Abstract
In this thesis, a monitoring system is designed and implemented for the Community Development Plan for the Mount Pleasant neighbourhood of Vancouver, British Columbia. The literature review first provides the context for plan monitoring by showing that the complexity of urban systems necessitates a continuous planning process, i.e. a cyclical or iterative linking of decision-making, implementation and monitoring in order that planning can adapt to changing community goals, issues, and trends. The role of monitoring in this continuous planning process is initially reviewed in terms of systems feedback and control. However, the complexity of urban systems suggests that concentrating on the goals and objectives of a plan provides too narrow a perspective for monitoring. An expanded role for plan monitoring, one that also addresses assumptions, policies, decisions and issues of concern, is reviewed in the context of a general monitoring system model. This model incorporates four sub-systems: information collection; technical evaluation; provision of advice; and monitoring system improvement. The case study is conducted in three stages: first, a monitoring system based on the four function model is designed for the Mount Pleasant Plan; second, monitoring systems requirements are specified for one section of the Plan, the Mount Pleasant industrial area strategy; and third, data are collected and analyzed for the still more specific policy to maintain existing residential use in the industrial area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".