Bibliographic record
Abstract
Roads are an important aspect of the efficiency gains that stem from population density: the more people live on a given road network, the less each person must pay for paving, maintenance, and snow clearing. While density is related to the road length per resident, the two variables are not synonymous. Two urban areas may have the same spatial extent and population, yet feature distinct road network morphologies, resulting in different values for road length per resident. Road length per resident measures a major category of costs directly, as a large proportion of many municipal budgets are dedicated to road maintenance. A better understanding of road length per resident can therefore support financially prudent urban development policy. The primary objective of this research is therefore to investigate how road length per resident varies with density between the sub-geographies of cities. Nine cities from across Canada were selected and the road length per resident and net density of their census tracts were calculated. The results present a strong and consistent non-linear association between population density and road length per resident. The present analysis is most valuable for distinguishing between medium-density and low-density suburbs. The results suggest that a shift may be necessary in how urban theorists communicate the costs of low-density growth.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.006 |
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".