Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Carsharing offers access to cars and other vehicles without ownership of those vehicles. This transportation option is growing rapidly in the United States and Canada. In appropriate community settings, carsharing can increase mobility, reduce vehicle travel, and complement other transportation modes. In a TCRP project that provided a wide-ranging analysis of carsharing in North America, direct contacts with carsharing members through focus groups and a web-based survey were used to determine demographic characteristics of users, their travel patterns, and their attitudes about carsharing. Special attention was paid to why members joined carsharing organizations, how they used the services, and what they liked and disliked about carsharing. With descriptive statistics from the Internet survey and qualitative analyses of focus group results (both checked against previous literature), it was determined that carsharing appeals to individuals who can be considered to be social activists, environmental protectors, innovators, economizers, or practical travelers. Carsharing companies and their partners could conceivably increase their membership by targeting such individuals and others with certain demographic characteristics.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it