Bibliographic record
Abstract
Shared-use vehicle services provide members access to a fleet of vehicles for use throughout the day, without the hassles and costs of individual auto ownership. From June 2001 to June 2002, the authors surveyed 28 North American shared-use vehicle service organizations on a range of topics, including business model approach, organizational size, strategic partnerships, pricing strategies, and technology applications. While survey findings demonstrate a decline in the number of organizational starts between June 2001-2002, the rate of operational launches into new cities, membership, and fleet size continue to increase. Several growth-oriented organizations in Canada and the U.S. are responsible for the majority of this growth and innovation. The authors also note several factors that could facilitate or inhibit shared-use vehicle market growth in North America, such as high capital investment (or start-up costs), dramatic hikes in insurance rates, grant programs and other supportive public policies, and technology developments. Based on survey findings, the authors conclude that the public and private sectors can play a key role in optimizing the economic potential and social benefits of shared-use vehicle systems in North America. Further, the authors recommend that policymakers and transit organizations continue monitoring shared-use vehicle program benefits and smart technology applications (particularly integration with transit fare collection) and fostering long-term system growth and sustainability through grant making, supportive public policies (e.g., parking), and strong public-private partnerships.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.508 | 0.326 |
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".