User interest in car sharing as an indicator of sustainable urban agglomeration development
Bibliographic record
Abstract
\nThe use of car sharing instead of owning a car minimises the negative impact of logistics activities on the urban environment. This research aims to show that sustainable development of densely populated cities is accompanied by an increase in Internet users’ interest in sharing services. The research of Internet users’ interest in car sharing services was based on Google Trends data on search queries originating from Russia, the United States and Canada over the past five years. In the course of this work, the hypothesis was confirmed that high user interest in car sharing is mainly observed in urban agglomerations with high population numbers and density, where the positive effects of car sharing are most noticeable. The paper emphasises the need to encourage the creation of new services in urban logistics, which will contribute to sustainable development and increase the competitiveness of cities. It also confirms the hypothesis that the growing interest of Internet users in the new service is accompanied by an increase in the market volume. User interest in established car sharing markets is at a stable level, except for the occurrence of significant events (e.g., the emergence of a new major player in the market) that stimulate an increase in interest.\n
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".