Bibliographic record
Abstract
Time use studies clearly document that human activities do not occur in a vacuum. Each individual activity is part of a spatial, temporal socio-economic system. Each activity is part of a system of activities that integrates and facilitates ongoing day-to-day behavior and it is inextricably linked to other activities, past, present and future. Cooperatively and/or independently, individuals and groups interact and make opportunity-changing choices impacting, at various levels, the activity systems of which they are a part. Unfortunately, the four-step approach, which for so long dominated transportation planning, failed to recognize this reality. A mechanistic approach that ignored the spatial, temporal, and individual interdependencies among transportation, land use, and population, it has left a legacy of urban areas with seriously inappropriate land use and transportation systems. The aggregate approach of the method to planning failed, to providing the guidance necessary to plan efficient, equitable, and sustainable land use and transportation systems. Fortunately, major shortcomings of the four-step approach are being overcome by a shift in thinking toward an activity-based planning approach. This chapter explores the development of activity-based planning and activity systems, identifies and elucidates activity-related data needs, and it discusses the important role and method of time use studies in supply such data.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".