Collaborative public transportation feasibility study: Development of a database prototype
Bibliographic record
Abstract
The Ecology Action Centre (EAC) has recognized the need for a collaborative information-based transit service that would connect users and transportation providers in the Maritime Provinces. From this need, a multi-disciplinary research partnership between the EAC and Dalhousie University was developed. The research team developed a basic functioning prototype for a web-based transit service. Four main methodological thrusts define the transit database project: first, a review of transit database precedents; second, a usability study of potential database users (n=6); third, a feasibility study reaching of transit providers (n=10); and lastly, web-expert consultation. Additionally, a number of potential funding sources for this project were identified, including community and government grants, web-based advertising and cooperative membership fees. Moreover, the identification of an effective entity under which the Go Maritimes service will be operated (i.e. multi-stakeholder cooperative or private sector enterprise) has been a high priority. This research project has equipped the EAC with the tools it needs to plan, manage, and move forward with the Go Maritimes project.
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 | 0.024 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".