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Record W2107651391 · doi:10.14236/ewic/hci2010.39

Googling Bing: Reassessing the impact of brand on the perceived quality of two contemporary search engines

2010· article· en· W2107651391 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueElectronic workshops in computing · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSearch engineQuality (philosophy)PerceptionSet (abstract data type)Search engine optimizationSalientComputer scienceSearch advertisingInformation retrievalPreferenceAdvertisingOnline searchValue (mathematics)PsychologyWorld Wide WebBusinessThe InternetOnline advertisingMathematicsArtificial intelligenceStatisticsMachine learning

Abstract

fetched live from OpenAlex

Given the high value of the online search market, whether brand perception or quality of search results matters more for users is a highly salient question. This paper presents findings of the largest controlled, systematic preference elicitation study of search quality versus brand perception. We examine a total of 548 instances of sponsored and organic search results from the Google and Bing search engines as rated by 25 participants. We find that, if users are not aware of the source of a set of search results, they will consistently rate Google results as better. However, the presence of the Google brand strongly influences perceived quality, essentially over-riding differences in search result quality. Together, these results demonstrate that, while Google may outperform Bing in blind searching, trust in the Google brand is a much more significant factor in users’ search preferences.

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.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.078
GPT teacher head0.425
Teacher spread0.348 · 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