Spatial Aggregation and Compactness of Census Areas with a Multiobjective Genetic Algorithm: A Case Study in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The paper focuses on a case study of delineating census tracts (CTs) in the Census Metropolitan area of London, Ontario, Canada. The procedure for defining the actual pattern of CTs by a local committee and Statistics Canada has involved such consideration as the compactness of CTs and their population-based and area-based uniformity as well as some subjective aspects. The actual pattern shows that compactness of CTs has been achieved at the expense of uniformity in population and areal sizes. The paper proposes an integer-coded multiobjective genetic algorithm for aggregating census units with the expectation of obtaining a higher level of compactness and population/area uniformity of CTs through an optimization technique. Square-shape and circular-shape compactness of CTs are examined under different scenarios. The results indicate that the proposed genetic algorithm can provide solutions that are considerably better in terms of the Pareto-optimality principle than the actual pattern of CTs.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it