Contextual Inferences, Nonlocality, and the Incompleteness of Quantum Mechanics

It is known that “quantum non locality”, leading to the violation of Bell’s inequality and more generally of classical local realism, can be attributed to the conjunction of two properties, which we call here elementary locality and predictive completeness. Taking this point of view, we show again that quantum mechanics violates predictive completeness, allowing the making of contextual inferences, which can, in turn, explain why quantum non locality does not contradict relativistic causality. An important question remains: if the usual quantum state ψ is predictively incomplete, how do we complete it? We give here a set of new arguments to show that ψ should be completed indeed, not by looking for any “hidden variables”, but rather by specifying the measurement context, which is required to define actual probabilities over a set of mutually exclusive physical events.

Effect of Context on Scientists’ Normative Beliefs

Past research on the relationship between scientists’ normative beliefs about public engagement in the context of willingness to engage could prove misleading if respondents do not consider the impacts of engagement activities when responding to survey questions. This study asks scientists to report normative beliefs in the context of engagement impacts and explores correlations between these responses and engagement willingness. Results suggest mentioning positive societal impact positively affects normative beliefs, while mentioning lost research time negatively affects normative beliefs. However, changing the measurement context does not affect the non-relationship between normative beliefs and engagement willingness.

Toward Automated Data Extraction: A Pilot Survey of the Structure of Tabular Data in Clinical Comparative Literature (Preprint)

BACKGROUND Systematic reviews depend on time-consuming extraction of data from PDFs of underlying studies. To date, automation efforts have focused on extracting from the text, and no approach has yet succeeded in fully automating ingestion of quantitative evidence. However, the majority of relevant data is generally presented in tables, and tabular structure is more amenable to automated extraction than free-text. OBJECTIVE The purpose of this survey is to classify the structure and format of descriptive statistics reported in tables in the comparative medical literature. METHODS We sampled 100 published randomized controlled trials (RCTs) from the year 2019 from PubMed; these results were imported to the AutoLit platform. Studies were excluded if they were non-clinical, non-comparative, not in English, protocol-only, or not available in full text. In AutoLit, tables reporting baseline or outcome data in all studies were characterized based on reporting practices. Measurement context, meaning the structure in which the interventions of interest, patient arm breakdown, measurement timepoints, and data element descriptions were presented, was classified based on the number of contextual pieces and metadata reported. Then, the statistic formats for reported metrics (specific instances of reporting of data elements) were classified by location and broken down into reporting strategies for continuous, dichotomous, and categorical metrics. RESULTS We included 78 of 100 studies, one of which (1.3%) did not report data elements in tables. The remaining 77 studies reported baseline and outcome data in 174 tables, and 97% of these tables broke down reporting by patient arms. Fifteen structures were found for the reporting of measurement context, which were broadly grouped into: 1x1 Contexts, where two pieces of context are reported total (e.g. “arms in columns, data elements in rows); 2x1 Contexts, where two pieces of context are given on row headers (e.g. timepoints in columns, arms nested in data elements on rows); 1x2 Contexts, where two pieces of context are given on column headers. 1x1 contexts were present in 57% of tables, compared to 20% for 2x1 and 15% for 1x2 (8% used unique/other stratification). Statistic formats were reported in the headers or descriptions of 84% of studies. CONCLUSIONS In this pilot survey, we found a high density of information in tables, but with major heterogeneity in presentation of measurement context. The highest-density studies reported both baseline and outcome measures in tables, with arm-level breakout, intervention labels and arm sizes present, and reported both the statistic formats and units. The measurement context formats presented here, broadly classified into three classes that cover 92% of studies, form a basis for understanding the frequency of different reporting styles, supporting automated detection of data format for extraction of metrics.

Reflecting on 25 Years of Research Using Implicit Measures: Recommendations for Their Future Use

For more than 25 years, implicit measures have shaped research, theorizing, and intervention in psychological science. During this period, the development and deployment of implicit measures have been predicated on a number of theoretical, methodological, and applied assumptions. Yet these assumptions are frequently violated and rarely met. As a result, the merit of research using implicit measures has increasingly been cast into doubt. In this article, we argue that future implicit measures research could benefit from adherence to four guidelines based on a functional approach wherein performance on implicit measures is described and analyzed as behavior emitted under specific conditions and captured in a specific measurement context. We unpack this approach and highlight recent work illustrating both its theoretical and practical value.

Measurement Error and Our Data

This chapter introduces the idea that all data are measured with error. It then uses a standard normally distributed error formulation within a state and measurement context to derive the optimal estimate of the measurement error given a data measurement.

TOFF, a database of traits of fish to promote advances in fish aquaculture

Functional traits can be valuable pieces of information for aquaculture research and management. Although fish traits have been the focus of an abundant research, trait datasets for these organisms are difficult to access and often unpractical to achieve meta-analyses without a time-consuming extensive review. Already available large-scale compilations include trait information for many fish species but not as detailed as required for aquaculture purpose. Here, we introduce the TOFF (i.e. Traits OF Fish), a database focusing on fish functional traits that aims at bringing together behavioral, morphological, phenological, and physiological traits always coupled to environmental measurement context into a single open-source access repository. TOFF hosts data from published field and experimental studies. Here, we release data for 228 traits for 174 species extracted from 165 publications and present a collaborative platform. We ultimately aim at providing an inclusive and accessible data resource to facilitate advances in aquaculture development.

Evidence of factorial validity and accuracy of attitudes in mental health scale

This study gathered evidence of factorial validity and internal consistency of the Attitudes in Mental Health Scale. Two studies were performed. A total of 404 college students, including women (69.6%) with a mean age of 24 years (Standard Deviation = 5.67), participated in Study 1. A total of 396 college students, including females (69.9%) with a mean age of 23 years (Standard Deviation = 5.41), participated in Study 2. In the first study, an exploratory factorial analysis indicated the existence of two factors (biomedical paradigm, α = 0.71; psychosocial paradigm, α = 0.66) with a total of 15 items. In the second study, a confirmatory factorial analysis indicated that the bifactorial model that was proposed for the scale was adequate: χ²/df = 2.41, GFI = 0.93 and RMSEA = 0.06, 90%IC [0.05 - 0.07]. The results concluded that this measure can be adequately applied in studies in the proposed measurement context.

The effect of monetary rewards on autonomous motivation in an enabling performance measurement context

This paper investigates the impact of monetary rewards on autonomous motivation in a context of an enabling performance measurement system (PMS). The study uses survey data from 314 managers. The results indicate that organizations benefit from PMSs that are perceived as highly enabling. A highly enabling PMS leads to a higher level of autonomous motivation, when compared to a situation with a minimally enabling PMS or no PMS at all. In companies where the PMS is perceived as minimally enabling, the results indicate that perceived fairness of individual monetary rewards positively affects managers’ autonomous motivation. The findings also reveal that the more the PMS is perceived as enabling, the less effective a fair individual bonus is for enhancing autonomous motivation