Developing the behavioural rules for an agentbased model of pedestrian movement
Bibliographic record
Abstract
The recent shift in emphasis towards walking as a safe, healthy and sustainable mode of transport presents a number of practical problems for the urban planner. Until recently, urban planning has largely placed the road user at the centre of infra-structural design, with significant implications for the perceived attractiveness of pedestrian environments (see the National Consumer Council Survey, 1987). This, together with more generalised changes in the pace of urban life and the dispersal of towns and cities, has played a significant role in the decreasing modal share of walking observed over the last quarter of the twentieth century. Our governments ’ recent commitments to reversing this decline is evidenced by their decision to charge local authorities with developing local Walking Strategies (DETR, 1998; Scottish Executive, 1998). Encouraging people to walk, however, is not trivial. In order to increase the number of pedestrian journeys within a given area, walking (either as a mode of transport, or as a leisure activity) must be made more attractive. However,
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".