Employment relocation, residential preference, and transportation mode choice: the case of the Justice Institute of BC [sic]
Bibliographic record
Abstract
Over the last 100 years technological improvements in urban travel in terms of reliability and speed, has meant increased mobility for residents. This was accelerated with the advent of the automobile. It allowed many to move to the suburbs that were typified by less expensive lower density housing, and commute longer distances to their place of work. Today, in urban areas, cars are the main means of urban transport. The problem arises in major urban areas across North American when everyone tries to travel at the same time (usually during to trip to and from work). Urban areas are faced with problems of congestion (during rush hour) along with the lack of attractive transit alternatives. One aspect of this problem is examined in terms commuting habits. The purpose of this exercise is to examine the commuting habits of Justice Institute employees whose place of work moves from the West Side of Vancouver to New Westminister. In the postmove period employees made a number of decisions regarding their modal-type and residential location. These decisions may have a significant impact on their activities and travel patterns in the city. The goal is to collect data that would indicate the place of residence of employees before and after the Justice Institute move. It should also include employee modal-type in the pre and postmove periods of the move. Such information is important in the understanding the changes' employees make regarding their residential location and modal-type and the reasons for these changes. As well, employee characteristics such as income can influence these decisions. Such decisions are based on employee's preferences, likes and dislikes regarding their neighbourhood and modal-type. Within this framework, it is the goal of this analysis to understand how employees make trade-offs between where they live and the time they spend commuting to and from work. The correlation parameter may describe the tendency for some commuters to locate themselves close to their employment. The analysis of the survey results will help planners understand more about the urban transport problem. Within this framework, planners can learn why people choose to travel by car instead of transit. This may be related to choice of neighbourhood. It may be that employees choose neighbourhoods that they like to live in regardless of their place of work. Thus, to understand more about the transport problem planners need to know what kinds of neighbourhoods attract people. If the quality of neighbourhoods is an important factor regarding employees' choice of residential location, any transport plan must include land-use initiatives that attempt to create neighbourhoods that attract people. The idea is to bridge the two; otherwise conflicting land-use policies could easily undermine any transport plan. Within this framework, policy must be geared to bring home and places of work closer together. This means creating vibrant neighbourhoods that contain a variety of land-use that could create more employment opportunities closer to home. Neighbourhoods should not only create just residential uses alone. That would mean people would have less distance to travel. This would also mean creating pedestrian and transit friendly neighbourhoods. Less emphasis would be given to the car and more to alternative methods of transport. Such policies can go along way in reducing the dependence on the car.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".