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Record W3118280471

Optimizing travel: opportunities for the U of M Fort Garry Campus

2009· dissertation· en· W3118280471 on OpenAlexaboutno aff
Tom Pearce

Bibliographic record

VenueMspace (University of Manitoba) · 2009
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicHospitality and Tourism Education
Canadian institutionsnot available
Fundersnot available
KeywordsForestryGeography
DOInot available

Abstract

fetched live from OpenAlex

This thesis examines transportation planning at the University of Manitoba Fort Garry campus with the view to improving efficiency, equity and reducing economic loss. Through a broad approach of Transportation Demand Management (TDM) a number of avenues are explored including a comprehensive literature review of sustainable transportation planning; the documentation of selected university TDM programs including University of Colorado, University of British-Columbia and the University of Ottawa; a University of Manitoba commuter web survey, and key informant interviews. Cost-benefit analysis, geographical information systems and key informants interviews are used. Twelve key recommendations are outlined in the concluding chapter. The research suggests optimal solutions can be reached if there is strong leadership from the University of Manitoba central administration in Transportation Demand Management (TDM) including a more collaborative approach to transportation and land use planning, as well as working closely with its stakeholders in reforming current practices. A series of incremental changes can give higher priority to walking, cycling, transit, and car pooling ahead of those driving alone resulting in a more equitable and efficient transportation system and leading to a healthier population and a healthier environment for the University of Manitoba community. The author can be contacted by email at tompearce@hotmail.com

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.906

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.218
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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