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Record W2269327109 · doi:10.1287/mnsc.2015.2301

One-Way Mirrors in Online Dating: A Randomized Field Experiment

2016· article· en· W2269327109 on OpenAlexaff
Ravi Bapna, Jui Ramaprasad, Galit Shmueli, Akhmed Umyarov

Bibliographic record

VenueManagement Science · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Market Behavior and Pricing
Canadian institutionsMcGill University
Fundersnot available
KeywordsAnonymityMatching (statistics)PopularityParallelsInternet privacyRandomized experimentFeature (linguistics)Field (mathematics)Computer sciencePsychologySocial psychologyComputer securityEngineeringStatistics

Abstract

fetched live from OpenAlex

The growing popularity of online dating websites is altering one of the most fundamental human activities: finding a date or a marriage partner. Online dating platforms offer new capabilities, such as extensive search, big data–based mate recommendations, and varying levels of anonymity, whose parallels do not exist in the physical world. Yet little is known about the causal effects of these new features. In this study we examine the impact of a particular anonymity feature, which is unique to online environments, on matching outcomes. This feature allows users to browse profiles of other users anonymously, by being able to check out a potential mate’s profile while not leaving any visible online record of the visit. Although this feature may decrease search costs and allow users to search without inhibition, it also eliminates “weak signals” of interest for their potential mates that may play an important role in establishing successful communication. We run a randomized field experiment on a major North American online dating website, where 50,000 of 100,000 randomly selected new users are gifted the ability to anonymously view profiles of other users. Compared with the control group, the users treated with anonymity become disinhibited, in that they view more profiles and are more likely to view same-sex and interracial mates. However, based on our analysis, we demonstrate causally that weak signaling is a key mechanism in achieving higher levels of matching outcomes. Anonymous users, who lose the ability to leave a weak signal, end up having fewer matches compared with their nonanonymous counterparts. This effect of anonymity is particularly strong for women, who tend not to make the first move and instead rely on the counterparty to initiate the communication. Further, the reduction in quantity of matches by anonymous users is not compensated by a corresponding increase in quality of matches. This paper was accepted by Lorin Hitt, information systems.

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 imitation

Not 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.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.034
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.001
Science and technology studies0.0030.006
Scholarly communication0.0030.003
Open science0.0050.002
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0120.002

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.

Opus teacher head0.042
GPT teacher head0.292
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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

Citations126
Published2016
Admission routes1
Has abstractyes

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