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1. Measuring Skin Color: Consistency, Comparability, and Meaningfulness of Rating Scale Scores and Handheld Device Readings

   https://doi.org/10.1093/jssam/smab046  

   Gordon, Rachel A. (April 2022, Journal of Survey Statistics and Methodology)

   As US society continues to diversify and calls for better measurements of racialized appearance increase, survey researchers need guidance about effective strategies for assessing skin color in field research. This study examined the consistency, comparability, and meaningfulness of the two most widely used skin tone rating scales (Massey–Martin and PERLA) and two portable and inexpensive handheld devices for skin color measurement (Nix colorimeter and Labby spectrophotometer). We collected data in person using these four instruments from forty-six college students selected to reflect a wide range of skin tones across four racial-ethnic groups (Asian, Black, Latinx, White). These college students, five study staff, and 459 adults from an online sample also rated forty stock photos, again selected for skin tone diversity. Our results—based on data collected under controlled conditions—demonstrate high consistency across raters and readings. The Massey–Martin and PERLA scale scores were highly linearly related to each other, although PERLA better differentiated among people with the lightest skin tones. The Nix and Labby darkness-to-lightness (L*) readings were likewise linearly related to each other and to the Massey–Martin and PERLA scores, in addition to showing expected variation within and between race ethnicities. In addition, darker Massey–Martin and PERLA ratings correlated with online raters’ expectations that a photographed person experienced greater discrimination. In contrast, the redness (a*) and yellowness (b*) undertones were highest in the mid-range of the rating scale scores and demonstrated greater overlap across race-ethnicities. Overall, each instrument showed sufficient consistency, comparability, and meaningfulness for use in field surveys when implemented soundly (e.g., not requiring memorization). However, PERLA might be preferred to Massey–Martin in studies representing individuals with the lightest skin tones, and handheld devices may be preferred to rating scales to reduce measurement error when studies could gather only a single rating. 

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2. Exploring author gender in book rating and recommendation

   https://doi.org/10.1007/s11257-020-09284-2  

   Ekstrand, Michael D.; Kluver, Daniel (February 2021, User Modeling and User-Adapted Interaction)

   null (Ed.)

   Collaborative filtering algorithms find useful patterns in rating and consumption data and exploit these patterns to guide users to good items. Many of these patterns reflect important real-world phenomena driving interactions between the various users and items; other patterns may be irrelevant or reflect undesired discrimination, such as discrimination in publishing or purchasing against authors who are women or ethnic minorities. In this work, we examine the response of collaborative filtering recommender algorithms to the distribution of their input data with respect to one dimension of social concern, namely content creator gender. Using publicly available book ratings data, we measure the distribution of the genders of the authors of books in user rating profiles and recommendation lists produced from this data. We find that common collaborative filtering algorithms tend to propagate at least some of each user’s tendency to rate or read male or female authors into their resulting recommendations, although they differ in both the strength of this propagation and the variance in the gender balance of the recommendation lists they produce. The data, experimental design, and statistical methods are designed to be reusable for studying potentially discriminatory social dimensions of recommendations in other domains and settings as well. 

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3. Multi-criteria and Review-Based Overall Rating Prediction

   https://doi.org/10.1007/978-3-030-75765-6_38  

   Ceh-Varela, Edgar; Cao, Huiping; Le, Tuan (January 2021, Pacific-Asia Conference on Knowledge Discovery and Data Mining)

   An overall rating cannot reveal the details of user’s preferences toward each feature of a product. One widespread practice of e-commerce websites is to provide ratings on predefined aspects of the product and user-generated reviews. Most recent multi-criteria works employ aspect preferences of users or user reviews to understand the opinions and behavior of users. However, these works fail to learn how users correlate these information sources when users express their opinion about an item. In this work, we present Multi-task & Multi-Criteria Review-based Rating (MMCRR), a framework to predict the overall ratings of items by learning how users represent their preferences when using multi-criteria ratings and text reviews. We conduct extensive experiments with three real-life datasets and six baseline models. The results show that MMCRR can reduce prediction errors while learning features better from the data. 

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4. ZePHyR: Zero-shot Pose Hypothesis Rating

   https://doi.org/10.1109/ICRA48506.2021.9560874  

   Okorn, Brian; Gu, Qiao; Hebert, Martial; Held, David (January 2021, International Conference of Robotics and Automation (ICRA))

   Pose estimation is a basic module in many robot manipulation pipelines. Estimating the pose of objects in the environment can be useful for grasping, motion planning, or manipulation. However, current state-of-the-art methods for pose estimation either rely on large annotated training sets or simulated data. Further, the long training times for these methods prohibit quick interaction with novel objects. To address these issues, we introduce a novel method for zero-shot object pose estimation in clutter. Our approach uses a hypothesis generation and scoring framework, with a focus on learning a scoring function that generalizes to objects not used for training. We achieve zero-shot generalization by rating hypotheses as a function of unordered point differences. We evaluate our method on challenging datasets with both textured and untextured objects in cluttered scenes and demonstrate that our method significantly outperforms previous methods on this task. We also demonstrate how our system can be used by quickly scanning and building a model of a novel object, which can immediately be used by our method for pose estimation. Our work allows users to estimate the pose of novel objects without requiring any retraining. 

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5. Pathogen Inoculation and Rating Strategies for Studying Maize Diseases

   https://doi.org/10.1101/pdb.top108447  

   Sorensen, Peyton; Mideros, Santiago; Jamann, Tiffany (June 2025, Cold Spring Harbor Protocols)

   Maize is a globally important staple that is used as food for human and animal consumption, fuel, and other industrial applications. Pathogens affect all stages of the plant life cycle and every plant organ, and lead to significant yield losses. An integrated strategy incorporating cultural and chemical management practices, as well as development of resistant plant varieties, is needed to prevent yield losses due to plant diseases. Large numbers of breeding material must be screened to develop pathogen-resistant maize varieties. Inoculation methods must be high-throughput to accommodate the large screening experiments. Additionally, there needs to be an extensive understanding of the plant–pathogen interaction to use a targeted biotechnology-based approach, which takes advantage of knowledge of the system to engineer resistance. To evaluate germplasm for breeding and biotechnology approaches, inoculation methods must replicate natural infection, and disease severity must be rated consistently to accurately screen germplasm or gather data on pathogens of interest. Here, we review inoculation and rating methods for Gibberella ear rot, seedling blight caused byGlobisporangium ultimumvar.ultimum, and Goss's wilt that are efficient and high-throughput. We also introduce fluorescence microscopy techniques for leaf samples infected withExserohilum turcicum, the causal agent of northern corn leaf blight. These pathogens all cause significant yield losses, and in particular, Gibberella ear rot is associated with the accumulation of harmful mycotoxins. Understanding how pathogens cause disease and how plants defend against attack is a major goal of maize pathology studies and critical for developing integrated management strategies. 

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