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Record W3209935317

The Sharing Economy: Do e-scooters Make the Cut?

2021· article· en· W3209935317 on OpenAlexaffabout
Brady Bailey

Bibliographic record

VenueStudent Research Proceedings · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsMacEwan University
Fundersnot available
KeywordsSharing economySustainabilityBusinessOrder (exchange)Sustainable businessResource (disambiguation)Social sustainabilityPublic relationsMarketingPolitical scienceComputer scienceFinance
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0060.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.102
GPT teacher head0.354
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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