Color-Blind Theories and Color-Conscious Remedies

Wright, J. Skelly

doi:10.2307/1599359

Cited by 7 publications

(8 citation statements)

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“…Rasch KIDMAP [54] has been used in educational fields [54–56] . All responses were compared with those of the Rasch model and examined by misfit statistics (ie, Z score = [observed score − expected value]/standard deviation).…”

Section: Methodsmentioning

confidence: 99%

“…[16,[39][40][41][42][43] As such, all the feature variables were applied to the CNN PMHB26 model. Rasch KIDMAP [54] has been used in educational fields. [54][55][56] All responses were compared with those of the Rasch model and examined by misfit statistics (ie, Z score = [observed score À expected value]/standard deviation).…”

Section: Rasch Catmentioning

confidence: 99%

“…Unsupervised and supervised learning. Unsupervised learning [16,53,54] was performed using k-means clustering, [55] which is a traditional, simple machine learning algorithm that is trained on a test dataset and then able to classify a new dataset using a prime (ie, k clusters) defined a priori. In contrast, supervised learning was preceded using the CNN model with the feature variables to predict the labels that were defined by k-means clustering.…”

Section: Rasch Catmentioning

confidence: 99%

“…Rasch KIDMAP [54] has been used in educational fields. [54][55][56] All responses were compared with those of the Rasch model and examined by misfit statistics (ie, Z score = [observed score À expected value]/standard deviation). Through KIDMAP, any aberrant behaviors on responses to the questionnaire were displayed and interpreted.…”

Section: Rasch Catmentioning

confidence: 99%

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An application for classifying perceptions on my health bank in Taiwan using convolutional neural networks and web-based computerized adaptive testing

2021

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Background: The classification of a respondent's opinions online into positive and negative classes using a minimal number of questions is gradually changing and helps turn techniques into practices. A survey incorporating convolutional neural networks (CNNs) into web-based computerized adaptive testing (CAT) was used to collect perceptions on My Health Bank (MHB) from users in Taiwan. This study designed an online module to accurately and efficiently turn a respondent's perceptions into positive and negative classes using CNNs and web-based CAT. Methods: In all, 640 patients, family members, and caregivers with ages ranging from 20 to 70 years who were registered MHB users were invited to complete a 3-domain, 26-item, 5-category questionnaire asking about their perceptions on MHB (PMHB26) in 2019. The CNN algorithm and k-means clustering were used for dividing respondents into 2 classes of unsatisfied and satisfied classes and building a PMHB26 predictive model to estimate parameters. Exploratory factor analysis, the Rasch model, and descriptive statistics were used to examine the demographic characteristics and PMHB26 factors that were suitable for use in CNNs and Rasch multidimensional CAT (MCAT). An application was then designed to classify MHB perceptions. Results: We found that 3 construct factors were extracted from PMHB26. The reliability of PMHB26 for each subscale beyond 0.94 was evident based on internal consistency and stability in the data. We further found the following: the accuracy of PMHB26 with CNN yields a higher accuracy rate (0.98) with an area under the curve of 0.98 (95% confidence interval, 0.97–0.99) based on the 391 returned questionnaires; and for the efficiency, approximately one-third of the items were not necessary to answer in reducing the respondents’ burdens using Rasch MCAT. Conclusions: The PMHB26 CNN model, combined with the Rasch online MCAT, is recommended for improving the accuracy and efficiency of classifying patients’ perceptions of MHB utility. An application developed for helping respondents self-assess the MHB cocreation of value can be applied to other surveys in the future.

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Section: Methodsmentioning

confidence: 99%

Section: Rasch Catmentioning

confidence: 99%

Section: Rasch Catmentioning

confidence: 99%

Section: Rasch Catmentioning

confidence: 99%

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An application for classifying perceptions on my health bank in Taiwan using convolutional neural networks and web-based computerized adaptive testing

2021

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“…Given the significant racial differences uncovered, policy needs to actively address these racial differences to make the playing field fair for all (Appiah and Gutmann 1998), reinforcing the need for color-conscious policies (Culp 1994). Wright (1980) and James (2000) view race-conscious policies as those that are based on a principle of equity that diminishes racial inequalities by compensating for past and present race discrimination. More recently race conscious are seen as being in the middle of the spectrum between color-blind policies and race-based policies and can be described as "policies designed to close racial gaps without targeting racial groups."…”

Section: Policy Implicationsmentioning

confidence: 99%

Racial Earnings Disparities with Endogenous Labor Market Participation

Lahiri

2018

J Econ Race Policy

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This paper examines racial disparity in wage income earnings in the USA across different quantiles, after accounting for the endogeneity of labor market engagement which is captured using the continuous variable: annual number of weeks worked. Controlling for this endogeneity is important because changes in income earned would affect the labor supply, while on the labor demand side any pressures that cause racial disparity is likely to affect both hiring and pay decisions. State-sector level averages for weather-related work absences are used as instruments for number of weeks worked at the individual level and robustness of the results are verified. The instrumental variable conditional and generalized quantile regression techniques are used for analyzing the disparity. The results find that the racial disparity gets larger at the upper quantiles even when controlling for other covariates ensure that the individuals are similar in important attributes.

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Identity Theft, Deep Brain Stimulation, and the Primacy of Post‐trial Obligations

Fins,

Merner,

Wright

et al. 2024

Hastings Center Report

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Patient narratives from two investigational deep brain stimulation trials for traumatic brain injury and obsessive‐compulsive disorder reveal that injury and illness rob individuals of personal identity and that neuromodulation can restore it. The early success of these interventions makes a compelling case for continued post‐trial access to these technologies. Given the centrality of personal identity to respect for persons, a failure to provide continued access can be understood to represent a metaphorical identity theft. Such a loss recapitulates the pain of an individual's initial injury or illness and becomes especially tragic because it could be prevented by robust policy. A failure to fulfill this normative obligation constitutes a breach of disability law, which would view post‐trial access as a means to achieve social reintegration through this neurotechnological accommodation.

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Color-Blind Theories and Color-Conscious Remedies

Cited by 7 publications

References 0 publications

An application for classifying perceptions on my health bank in Taiwan using convolutional neural networks and web-based computerized adaptive testing

An application for classifying perceptions on my health bank in Taiwan using convolutional neural networks and web-based computerized adaptive testing

Racial Earnings Disparities with Endogenous Labor Market Participation

Identity Theft, Deep Brain Stimulation, and the Primacy of Post‐trial Obligations

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