The Transit Node model : an integrative land-use and transportation planning alternative for suburban Winnipeg
Bibliographic record
Abstract
The research undertaken in this practicum involves examining the integration of transportation with land-use planning models that are appropriate to Winnipeg.Conventional transportation planning that gives priority to automobile commuting is increasingly being re- examined because it inherently fails to rectiff social and physical urban problems such as social isolation, and inequities in employment opportunities.Moreover, the separation of land-use and transportation planning has encouraged urban sprawl, wíth all daily activities being segregated from each other.The intent of this practicum is to explore various innovative solutions in major cities and supportive planning models, particularly the Transit Node concept, which attempts to address some of these concerns.A Transit Node is defined as a suburban centre that fosters a job-housing balance and higher-density development; integrated with transit use.The study is divided into two parts: the first consists of relevant literature review of precedents to derive guiding principles of the Transit Node model; the second involves the charleswood case study.Three potential infill Transit Nodes (T-Nodes #1, #3,and #5) have been identified from the study, which could support public transportation in the community, public transportation is defined as all commuting modes that support the mass public.By adhering to the principles of a job-housing balance, with higher-density and mixed-use development, the proposed solutions to Charleswood include two plausible scenariosthe first potentially designating all three areas as Transit Nodes, while the second proposes only two of the three, namely T-Nodes #1 and #5.Both scenarios have their own benefits, as well as necessary conditions for implementation.Overall, the nine principles assisted in providing strong visions for a more public transportation-supportive planning and development, as identified by the objectives of the study.contributed their efforts in assisting with the research and writing of the practicum.First, I want to thank my committee, especially Prof. Basil ngprf,who have irovided'me with much guidance, wisdom, and humour along the way.Your willingness to wori with me is an honour that I shall che.rish.Secondly, I want to
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".