Sentence examples for discriminatory selection from inspiring English sources

It seems that ABC's millionaire-game-show phenomenon now has the country buzzing over its arguably discriminatory selection process.

However, discriminatory selection criteria, limited coverage of health care needs and high premium rates [ 30] raise the question as to whether these schemes can meet fully the health care needs of vulnerable groups such as the elderly and low income families.

Mr. Banks's current defense lawyers had cited indifferent defense counsel during the trial, discriminatory jury selection and perjured testimony of two key witnesses.

In Alabama, courts have found racially discriminatory jury selection in 25 death penalty cases since 1987, and there are counties where more than 75 percent of black jury pool members have been struck in death penalty cases.

Is the judge limited to considering the plausibility of the prosecution's particular explanations, or can the judge consider evidence the defense might have offered, back at the initial stage, of a wider pattern of discriminatory jury selection throughout the jurisdiction?

According to our participants' statements, the reason for this is related to FPs' discriminatory patient selection practices and the fact that most FPs have lists of more than 4,000 patients.

The second component is related to the introduction of incentive-payment schemes and constitutes the main underlying reason of problems regarding the discriminatory patient-selection practices of FPs, "supplier-reduced" demand through the decline of consultation time, and underprovision of services not included in the list of target payments.

The discriminatory motif feature selection approach that we employed has the added potential to facilitate interpretation of the biological mechanism underlying the classifier performance.

Algorithms that facilitate interpretation, such as discriminatory motif feature selection, have the added potential to impart information about underlying biological mechanism.

Here, we investigate the impact of using appropriate data pre-processing —deduplication, normalization, and position- (peak-) finding to identify stable nucleosome positions — in conjunction with advanced classification algorithms, notably discriminatory motif feature selection and random forests.

Further, for DNA sequence-based classification, we utilize the recently developed Discriminatory Motif Feature Selection (DMFS) [ 22], which, in addition to achieving impressive accuracy, emphasizes interpretability, unlike so-called "black-box" classifiers.