Seal of Approval? Trust signals and cultural distance in a global peer-to-peer platform market
Bibliographic record
Abstract
Trust is essential for fostering cooperation, especially in global peer-to-peer platform markets where transactions between strangers involve significant risks and uncertainties. The global scope of these platforms introduces cultural differences, further intensifying these challenges. It is well established that social distance shapes trust, with decision-makers typically favoring those who share similarities, leading to trust disparities that advantage some participants while disadvantaging others. But existing theories offer conflicting perspectives on whether quality signals can bridge or exacerbate the gap between advantaged (i.e., socially proximate to focal decision-makers) and disadvantaged (i.e., socially distant from focal decision-makers) participants. Drawing on sociological theories of trust production that highlight how various social systems act as different sources of trust, we offer a new perspective to this puzzle by comparing two types of quality signals: reputation, which is derived from prior exchanges and provided by prior exchange partners, and institutional accreditation, which is linked to organizational institutions. Analyzing a proprietary dataset from a global peer-to-peer lodging platform, we find that prospective guests who are more culturally distant from hosts are in a disadvantaged position: their lodging requests are less likely to be approved by hosts. Furthermore, the positive effect of guest reputation (i.e., ratings) is weaker for culturally distant guests, and thus widens the gap in host acceptance of culturally proximate versus culturally distant guests. By contrast, the positive effect of institutional accreditation (i.e., platform verification) is stronger for culturally distant guests, indicating that it helps narrow the gap. These findings reveal unexplored contingencies to theories of evaluations bias and discrimination, contributing to the broader literature on trust, culture, and inequality in global online markets, and underscoring the challenges of building trust in uncertain environments.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".