Bibliographic record
Abstract
The OCAD University Visual Analytics Laboratory (VAL) has developed a taxonomy of end users, software systems, data types, tasks and interactivity within the domain of smart city transportation planning. This paper contributes to the taxonomy by creating Compara, an intuitive, interactive and searchable index that visualizes the attributes of software from a wide-range of applications and technologies. The taxonomy began as a spreadsheet that we transformed into a custom interactive data visualization that could help users find and understand existing tools and their attributes. The taxonomy and interactive index are a component of the iCity project which brings together academic, government, and industrial partners in order to improve the quality of life for urban residents and visitors through the development and integration of advanced IT infrastructure for the purpose of managing transit and transportation. The taxonomy and resulting tools discussed in this paper have expanded to include resources for urban planning which also impact transit and transportation. One of the primary uses of the taxonomy is to help various iCity project teams and stakeholders locate software that may be useful to their design and development process, as well as to understand the end-users of this technology. The taxonomy may be used to develop a city management dashboard for city planners and analysts, or equally, to design a city-services facing mobile service application.
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 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.095 | 0.026 |
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".