Bibliographic record
Abstract
We identify some of the underlying processes that support decision-making activities undertaken by three public transit stakeholders in Ottawa, and evaluate perceived usability of the proposed technological component of a novel public transit decisionmaking information system: a database query and results visualization tool (i.e., a cybercartographic atlas prototype).We highlight significant vulnerabilities in existing public transit decision-making processes, including the presence of common biases and heuristics, wherever human judgment is exercised.Our prototype is designed to Thank you to Professor Robert Biddle for taking a chance on me -for allowing me the opportunity to enrol in two graduate-level courses while not formally admitted to the program, and for your guidance and support throughout this process.Your kindness truly knows no bounds.Thank you to Professor Fraser Taylor -your mentorship and exceptional communication skills have enabled me to grow as a person and young professional.Your commitment to treating people the right way, and emphasis on building and maintaining relationships, is something that I will keep with me moving forward.You embody humility in light of dynamism, thank you.Thank you to my therapist Doug -you allowed me the freedom to work through some difficult times at my own pace, and introduced me to your former colleague Daniel Kahneman's body of work.You listened for understanding and helped me find perspective in life with thoughtful reading suggestions and an uncanny ability to label my thoughts.Finally, to my loving family
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".