Lessons learned from inter-laboratory studies of carbon isotope analysis of honey

Forensic application of carbon isotope ratio measurements of honey and honey protein to investigate the degree of adulteration with high fructose corn syrup or other C₄ plant sugars is well established. These measurements must use methods that exhibit suitable performance criteria, particularly with regard to measurement uncertainty and traceability – low levels of adulteration can only be detected by methods that result in suitably small measurement uncertainties such that differences of 1‰ or less can be reliably detected. Inter-laboratory exercises are invaluable to assess the state-of-the art of measurement capabilities of laboratories necessary to achieve such performance criteria. National and designated metrology institutes from a number of countries recently participated in an inter-laboratory assessment (CCQM-K140) of stable carbon isotope ratio determination of bulk honey. The same sample material was distributed to a number of forensic isotope analysis laboratories that could not participate directly in the metrological comparison. The results from these studies have demonstrated that the majority of participants provided isotope delta values with acceptable performance metrics; that all participants ensured traceability of their results; and that where measurement uncertainties were reported; these were fit-for-purpose. A number of the forensic laboratories only reported precision rather than full estimates of measurement uncertainty and this was the major cause of the few instances of questionable performance metrics. Reporting of standard deviations in place of measurement uncertainties is common practice outside metrology institutes and the implications for interpretations of small differences in isotopic compositions are discussed. The results have also highlighted a number of considerations that are useful for organisers of similar inter-laboratory studies in the future.     

Using Named Entities for Computer‐Automated Verbal Deception Detection

There is an increasing demand for automated verbal deception detection systems. We propose named entity recognition (NER; i.e., the automatic identification and extraction of information from text) to model three established theoretical principles: (i) truth tellers provide accounts that are richer in detail, (ii) contain more contextual references (specific persons, locations, and times), and (iii) deceivers tend to withhold potentially checkable information. We test whether NER captures these theoretical concepts and can automatically identify truthful versus deceptive hotel reviews. We extracted the proportion of named entities with two NER tools (spaCy and Stanford's NER) and compared the discriminative ability to a lexicon word count approach (LIWC) and a measure of sentence specificity (speciteller). Named entities discriminated truthful from deceptive hotel reviews above chance level, and outperformed the lexicon approach and sentence specificity. This investigation suggests that named entities may be a useful addition to existing automated verbal deception detection approaches.     

Labor Market Returns to MBAs From Less-Selective Universities: Evidence From a Field Experiment During COVID-19

Master's degree enrollment and debt have increased substantially in recent years, raising important questions about the labor market value of these credentials. Using a field experiment featuring 9,480 job applications submitted during the early months of the COVID-19 pandemic, I examine employers’ responses to job candidates with a Master of Business Administration (MBA), which represents one-quarter of all master's degrees in the United States. I focus on MBAs from three types of less-selective institutions that collectively enroll the vast majority of master's students: for-profit, online, and regional universities. Despite the substantial time and expense required for these degrees, job candidates with MBAs from all three types of institutions received positive responses from employers at the same rate as candidates who only had a bachelor's degree—even for positions that listed a preference for a master's degree. Additionally, applicants with names suggesting they were Black men received 30 percent fewer positive responses than otherwise equivalent applicants whose names suggested they were White men or women, providing further evidence of racial discrimination in hiring practices.