English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Rankings and scores are two common data types used by judges to expresspreferences and/or perceptions of quality in a collection of objects. Numerousmodels exist to study data of each type separately, but no unified statisticalmodel captures both data types simultaneously without first performing dataconversion. We propose the Mallows-Binomial model to close this gap, whichcombines a Mallows' $\phi$ ranking model with Binomial score models throughshared parameters that quantify object quality, a consensus ranking, and thelevel of consensus between judges. We propose an efficient tree-searchalgorithm to calculate the exact MLE of model parameters, study statisticalproperties of the model both analytically and through simulation, and apply ourmodel to real data from an instance of grant panel review that collected bothscores and partial rankings. Furthermore, we demonstrate how model outputs canbe used to rank objects with confidence. The proposed model is shown tosensibly combine information from both scores and rankings to quantify objectquality and measure consensus with appropriate levels of statisticaluncertainty.

A Unified Statistical Learning Model for Rankings and Scores with Application to Grant Panel Review