Track recommendation bias:Gender, migration background and SES bias over a 20-year period in the Dutch context

Bias in track recommendations is an important mechanism, which causes education inequity in a tracked educational system (streaming). If teacher biases in track recommendations change over time, inequity in society and in the education system may also change. We investigated changes in track recommendation bias over time for gender, immigration status and socioeconomic status (SES), based on a longitudinal empirical study of nine cohorts of Dutch students in their final year (grade 6) of primary education in the period 1995-2014. An overview of educational and societal trends was provided, alongside the empirical analysis, to explain the findings in variation over time in track recommendation bias. Results indicate that the level of track recommendations provided to the students gradually increased over time. For a similar performance, a higher track recommendation was awarded in 2014 compared to 1995. This development coincided with an increase in parental education level, the valuing of education and the introduction of lower-status pre-vocational education tracks. Track recommendation bias favouring students with a migrant background and female students decreased, which coincided with growing cultural intolerance and attention to the boy problem'. Bias in track recommendations related to SES appeared stable, with only small deviations from year to year. The results of this study indicate that track recommendation bias and teacher considerations are dependent on time and context

An Integrated Quantitative Assessment of Urban Water Security of a Megacity in the Global South

Water security, the access to adequate amounts of water of adequate quality, is and will remain a hugely important issue over the next decades as climate change and related hazards, food insecurity, and social instability will exacerbate insecurities. Despite attempts made by researchers and water professionals to study different dimensions of water security in urban areas, there is still an absence of comprehensive water security measurement tools. This study aims to untangle the interrelationship between biophysical and socio-economic dimensions that shape water security in a megacity in the Global South—Kolkata, India. It provides an interdisciplinary understanding of urban water security by extracting and integrating relevant empirical knowledge on urban water issues in the city from physical, environmental, and social sciences approaches. To do so we use intersectional perspectives to analyze urban water security at a micro (respondent) level and associated challenges across and between areas within the city. The study concludes with the recommendation that future studies should make use of comprehensive and inclusive approaches so we can ensure that we leave no one behind

On Integrating Student Empirical Software Engineering Studies with Research and Teaching Goals

Background: Many empirical software engineering studies use students as subjects and are conducted as part of university courses. Aim: We aim at reporting our experiences with using guidelines for integrating empirical studies with our research and teaching goals. Method: We document our experience from conducting three studies with graduate students in two software architecture courses. Results: Our results show some problems that we faced when following the guidelines and deviations we made from the original guidelines. Conclusions: Based on our results we propose recommendations for empirical software engineering studies that are integrated in university courses.

How Do Analyst Recommendations Respond to Major News?

We examine how analysts respond to public information when setting stock recommendations. We model the determinants of analysts’ recommendation changes following large stock price movements. We find evidence of an asymmetry following large positive and negative returns. Following large stock price increases, analysts are equally likely to upgrade or downgrade. Following large stock price declines, analysts are more likely to downgrade. This asymmetry exists after accounting for investment banking relationships and herding behavior. This result suggests recommendation changes are “sticky” in one direction, with analysts reluctant to downgrade. Moreover, this result implies that analysts’ optimistic bias may vary through time

Lost in Translation: Piloting a Novel Framework to Assess the Challenges in Translating Scientific Uncertainty From Empirical Findings to WHO Policy Statements.

BACKGROUND:Calls for evidence-informed public health policy, with implicit promises of greater program effectiveness, have intensified recently. The methods to produce such policies are not self-evident, requiring a conciliation of values and norms between policy-makers and evidence producers. In particular, the translation of uncertainty from empirical research findings, particularly issues of statistical variability and generalizability, is a persistent challenge because of the incremental nature of research and the iterative cycle of advancing knowledge and implementation. This paper aims to assess how the concept of uncertainty is considered and acknowledged in World Health Organization (WHO) policy recommendations and guidelines. METHODS:We selected four WHO policy statements published between 2008-2013 regarding maternal and child nutrient supplementation, infant feeding, heat action plans, and malaria control to represent topics with a spectrum of available evidence bases. Each of these four statements was analyzed using a novel framework to assess the treatment of statistical variability and generalizability. RESULTS:WHO currently provides substantial guidance on addressing statistical variability through GRADE (Grading of Recommendations Assessment, Development, and Evaluation) ratings for precision and consistency in their guideline documents. Accordingly, our analysis showed that policy-informing questions were addressed by systematic reviews and representations of statistical variability (eg, with numeric confidence intervals). In contrast, the presentation of contextual or "background" evidence regarding etiology or disease burden showed little consideration for this variability. Moreover, generalizability or "indirectness" was uniformly neglected, with little explicit consideration of study settings or subgroups. CONCLUSION:In this paper, we found that non-uniform treatment of statistical variability and generalizability factors that may contribute to uncertainty regarding recommendations were neglected, including the state of evidence informing background questions (prevalence, mechanisms, or burden or distributions of health problems) and little assessment of generalizability, alternate interventions, and additional outcomes not captured by systematic review. These other factors often form a basis for providing policy recommendations, particularly in the absence of a strong evidence base for intervention effects. Consequently, they should also be subject to stringent and systematic evaluation criteria. We suggest that more effort is needed to systematically acknowledge (1) when evidence is missing, conflicting, or equivocal, (2) what normative considerations were also employed, and (3) how additional evidence may be accrued

The use of intellectual capital information by sell-side analysts in company valuation

This paper investigates the role of intellectual capital information (ICI) in sell-side analysts’ fundamental analysis and valuation of companies. Using in-depth semi-structured interviews, it penetrates the black box of analysts’ valuation decision-making by identifying and conceptualising the mechanisms and rationales by which ICI is integrated within their valuation decision processes. We find that capital market participants are not ambivalent to ICI, and ICI is used: (1) to form analysts’ perceptions of the overall quality, strengths and future prospects of companies; (2) in deriving valuation model inputs; (3) in setting price targets and making investment recommendations; and (4) as an important and integral element in analyst–client communications. We show that: there is a ‘pecking order’ of mechanisms for incorporating ICI in valuations, based on quantifiability; IC valuation is grounded in valuation theory; there are designated entry points in the valuation process for ICI; and a number of factors affect analysts’ ICI use in valuation. We also identify a need to redefine ‘value-relevant’ ICI to include non-price-sensitive information; acknowledge the boundedness and contextuality of analysts’ rationality and motives of their ICI use; and the important role of analyst–client meetings for ICI communication

Evaluating Digital Tools for Sustainable Agriculture using Causal Inference

In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of climate-smart farming tools. Even though AI-driven digital agriculture can offer high-performing predictive functionalities, it lacks tangible quantitative evidence on its benefits to the farmers. Field experiments can derive such evidence, but are often costly and time consuming. To this end, we propose an observational causal inference framework for the empirical evaluation of the impact of digital tools on target farm performance indicators. This way, we can increase farmers' trust by enhancing the transparency of the digital agriculture market, and in turn accelerate the adoption of technologies that aim to increase productivity and secure a sustainable and resilient agriculture against a changing climate. As a case study, we perform an empirical evaluation of a recommendation system for optimal cotton sowing, which was used by a farmers' cooperative during the growing season of 2021. We leverage agricultural knowledge to develop a causal graph of the farm system, we use the back-door criterion to identify the impact of recommendations on the yield and subsequently estimate it using several methods on observational data. The results show that a field sown according to our recommendations enjoyed a significant increase in yield (12% to 17%).Comment: Accepted for publication and spotlight presentation at Tackling Climate Change with Machine Learning: workshop at NeurIPS 202