cantnlp@LT-EDI@RANLP-2023: Homophobia/Transphobia Detection in Social Media Comments using Spatio-Temporally Retrained Language Models

This paper describes our multiclass classification system developed as part of the LTEDI@RANLP-2023 shared task. We used a BERT-based language model to detect homophobic and transphobic content in social media comments across five language conditions: English, Spanish, Hindi, Malayalam, and Tamil. We retrained a transformer-based crosslanguage pretrained language model, XLMRoBERTa, with spatially and temporally relevant social media language data. We also retrained a subset of models with simulated script-mixed social media language data with varied performance. We developed the best performing seven-label classification system for Malayalam based on weighted macro averaged F1 score (ranked first out of six) with variable performance for other language and class-label conditions. We found the inclusion of this spatio-temporal data improved the classification performance for all language and task conditions when compared with the baseline. The results suggests that transformer-based language classification systems are sensitive to register-specific and language-specific retraining

Multidimensional Signals and Analytic Flexibility: Estimating Degrees of Freedom in Human-Speech Analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis that can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling but also from decisions regarding the quantification of the measured behavior. In this study, we gave the same speech-production data set to 46 teams of researchers and asked them to answer the same research question, resulting in substantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further found little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise, or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system, and calibrate their (un)certainty in their conclusions

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

Multidimensional signals and analytic flexibility: Estimating degrees of freedom in human speech analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis which can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling, but also from decisions regarding the quantification of the measured behavior. In the present study, we gave the same speech production data set to 46 teams of researchers and asked them to answer the same research question, resulting insubstantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further find little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system and calibrate their (un)certainty in their conclusions

So you want to farm - What does it take? - What can you make?

File: Ag. Econ. 3 4/73/10M"Farming means different things to different people. To some it is a commercial business where the farm manager employs his talents in producing food and fiber, thereby earning a return for his labor and equity. To others, it is a way of life - a good place to raise a family and escape some of the frustrations of urban living. While the farm need nore provide the sole income for the family, it can supplement other income and greatly add to the family's living and enjoyment."--First page.Thomas G. Brown and Durward Brewer (Dept. of Agricultural Economics, College of Agriculture

Aspectos da mensuracao do movimento functional humano ( Measurement issues in functional human movement)

This chapter measures the functions of human movement

Mensuracao e analise do movimento functional humano o estado da arte (Measurement and analysis of functional human movement - the state of the art)

This examines the functions of human movement

Functional Human Movement: Measurement and Analysis

In one succinct volume this book presents an overview of the analysis of human movement. The initial chapters present the key issues related to measuring human movement and relate these to the clinical environment. Important scientific and practical issues are discussed such as the accuracy, precision and calibration of measurement devices; the range of parameters available to describe functional movement and the nature and availability of clinical measurement tools. Subsequent chapters present authoritative reviews of different human functions from leading researchers in the field. These describe the current knowledge related to that function and the methods by which it can be evaluated. Finally the editors present a personal interpretation of the state of art in measuring functional human movement and indicate further avenues for exploration