Towards a better implementation of accessibility indicators in land use and transport planning practice
Bibliographic record
Abstract
ACKOWLEDGMENTSDuring my four years at the McGill School of Urban Planning, I have had the chance to be surrounded by great people who allowed me to make the most of this journey.I would like to thank and acknowledge them for making the process and completion of this dissertation possible, and enjoyable!First, it is a pleasure to express my sincere thanks to my supervisor, Professor Ahmed El-Geneidy, for his constant support and availability, his dedication to supervision as well as his passion for research.I am sincerely grateful for the countless brainstorm sessions and exchange of ideas we had over the years.Through these discussions, Ahmed inspired me on how to conduct research with passion, always striving for innovative and meaningful approaches.I also deeply appreciate the great deal of opportunities (conferences, workshops, media interviews, research projects as well as teaching and supervision tasks) that Ahmed provided me with -more than I could ever have imagined when I first started the PhD.These diverse experiences allowed me to develop extremely valuable personal and professional skills beyond the scope of my doctoral project.Finally, I am very thankful for the collaborative and enjoyable working environment that Ahmed has put in place: my PhD journey would not have been the same without this!I would also like to genuinely thank Professor Madhav Badhami and Professor David Wachsmuth, members of my committee, whose approaches to research were of great inspiration to me.A very special thanks also to Ludwig Desjardins, also member of my committee, who provided me with feedback enriched from his several years of experience in transport planning.This was extremely helpful for anchoring my research in practice.
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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 | 0.091 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".