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Record W7093401127

Curry v. Yelp

2015· article· W7093401127 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2015
Typearticle
Language
FieldMedicine
TopicLegal Cases and Commentary
Canadian institutionsnot available
Fundersnot available
KeywordsPlaintiffProspectusReputationFalse advertisingRevenueStock (firearms)CopycatQuarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

A. Allegations in the First Amended ComplaintYelp is a company founded in 2004 that "describes itself generally as an online networking platform that connects people with great local businesses" by hosting user-generated reviews.FAC 3. Yelp generates revenue by selling advertising on its website and mobile application.Id.United States District Court Northern District of California 4. Yelp held its Initial Public Offering in March 2012, and its shares are traded on the New York Stock Exchange under ticker symbol "YELP."Id. 3.This putative class action is brought on behalf of "all persons who purchased or otherwise acquired the common stock of Yelp from October 29, 2013, through April 3, 2014, inclusive," the "Class Period."Id. 1. Plaintiffs allege that Defendants made false and misleading statements regarding Yelp's advertising practices and financial condition, causing Yelp's stock to trade at "artificially-inflated prices during the Class Period."Id. 7. Plaintiffs allege that the misrepresentations began with Yelp's October 29, 2013 press release announcing the company's financial results for the Third Quarter of 2013, as well as the accompanying Prospectus and Registration Statement released on the same day.Id. 8.These materials stated that Yelp "saw another quarter of strong momentum thanks to the high-quality, authentic content contributed by Yelpers around the world" and that Yelp "contributors provide rich, firsthand information about local businesses, such as reviews, ratings and photos."Id.The Registration Statement acknowledged that "[t]he media has previously reported allegations that we manipulate our reviews, rankings and rating in favor of our advertisers and against nonadvertisers.These allegations, though untrue, could adversely affect our reputation and brand."Id. 9.Although Defendants disclosed both before and during the Class Period that Yelp actively curated and controlled the presentation of reviews on its website, Defendants consistently denied manipulating businesses' reviews in exchange for payment.Id. 12, 45.For instance, Yelp touted its use of recommendation software to curate reviews in order to ensure the authenticity, quality, and integrity of the reviews hosted on its website.Id. 33(a), (e).Vince Sollitto, Yelp'sVice President of Communications and Public Affairs, stated that Yelp employed algorithms as a "very aggressive means of filtering out attempts to game the system, fakes and shills."Id.Yelp also admitted that it controlled reviews and content through the assistance of "community managers" and "scouts" (paid Yelp employees who write reviews of local businesses).Id. 33(c), (d).In addition to its use of automated filtering algorithms, Yelp also employed agents who

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.013

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 head0.033
GPT teacher head0.287
Teacher spread0.254 · 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; both teacher heads agree on what is shown here.

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
Published2015
Admission routes1
Has abstractyes

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