Bibliographic record
Abstract
Sharing is as old as civilization itself. Corporations are now taking an old idea and creating a strategic model with the help of technology. This modern sharing economy, while having roots in sustainable practices, can often be mistaken as an inherently sustainable business model. In this paper, we present the outcomes of a project on e-scooters as an example that emphasizes the potential impacts and characteristics of a business operating within the sharing economy. To understand and gain public opinion, a survey was conducted gathering 222 responses regarding e-scooter usage in Edmonton, Alberta. Another source of information was the interview with a top executive of Lime Scooters, an e-scooter company operating in Edmonton. We found that while online platforms make resource sharing between peers easier to access, they are not always economically sustainable. Literature review on life-cycle analysis of e-scooters revealed that environmental sustainability is also not ingrained in practice, and careful consideration of business operations is needed to mitigate potentially negative impacts. In addition, thoughtful policies need to be considered and put into place in-order to encourage public and private trust. Overall, the sharing economy can be quite effective in creating a sense of community and social sustainability, but it should not be graded as a wholly sustainable practice without evidence. Department: Business Faculty Mentor: Dr. Rohit Jindal
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.024 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".