Bibliographic record
Abstract
Carsharing provides members access to a fleet of autos for short-term use throughout the day, reducing the need for one or more personal vehicles. More than 10 years ago, carsharing operators began to appear in North America. Since 1994, 40 programs have been deployed—28 are operating in 36 urban areas, and 12 are now defunct. Another four are planned to launch in the next year. Carsharing growth potential in North America is examined on the basis of a survey of 26 existing organizations conducted from April to July 2005. Since the mid-1990s, the number of members and vehicles supported by carsharing in the United States and Canada has continued to grow, despite program closures. The three largest providers in the United States and Canada both support 94% of the total carsharing membership. Growth potential in major metropolitan regions is estimated at 10% of individuals over the age of 21 in North America. Although carsharing continues to gain popularity and market share, the authors conclude that increased carsharing education, impact evaluation, and supportive policy approaches, including mainstreaming carsharing as a transportation strategy, would aid the ongoing expansion and development of this alternative to private vehicle ownership.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".