Trust and Reputation in the Sharing Economy: Toward a Peer-to-Peer Ethics
Bibliographic record
Abstract
The sharing economy and peer-to-peer business relationships using information technology has become moreimportant in today’s world. For the sharing economy to work, however, trust and reputation are cruciallyimportant. I argue that the gathering of personal data needs to be accompanied by safeguards providing aguarantee of privacy rights. This argument will be based on a sketch of a theory called ‘peer-to-peer ethics.’Basically, the idea is that what constitutes the ground for normativity is something that is agreed upon byeveryone involved. In short, what is considered to be ‘good’ is whatever contributes to bringing about thedesired goal of the community. This is a very familiar and ancient view on normative concepts, but, as I argue,one that deserves to be taken seriously especially as we enter into an intricately globalized world of ethicswhere worldviews clash with one another.
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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 | 0.023 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.048 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".