Prenatal Paranoia: An Analysis of the Bumpy Landscape for the Pregnant Athlete

ABSTRACT

I analyze the case of pregnant athletes, and argue that sexism surrounds pregnant athlete’s participation in sport. I claim that we stigmatize the pregnant body in action. Participating in sport while pregnant is often considered socially irresponsible behaviour because of unfounded fear of harming the fetus. Many believe that elite sport is in conflict with reproductive ability. However, this belief is not rooted in scientific findings, and rather is reinforced by traditional sexist views of the woman’s athletic body and its capabilities. Analyzing the various degrees of sexism like sexual objectification and unjust discrimination that surround the pregnant athlete is a relevant and necessary exercise in order to challenge traditional sexist views and increase agency for the pregnant athlete.     

Problem definition and information provision by federal bureaucrats

Federal bureaucrats are important sources of information about policy problems. However, federal officials compete for this influence with organized interests plying their own problems and solutions. We attribute the differential agenda influence of the federal bureaucracy to efforts in Congress to construct workable problem definitions in a context of uncertainty about issues. From both behavioral and rational models of congressional decision making, we develop a theory of congressional search for information during problem definition under conditions of uncertainty. The theory presages the prominence of federal bureaucrats in this search, and especially under uncertainty. Using new data sets capturing the appearance of federal bureaucrats at congressional hearings, we find that the mobilization, prominence, and types of federal bureaucrats providing information is explainable in terms of congressional uncertainty about problem definitions.     

Memory for goals: an activation‐based model

Goal‐directed cognition is often discussed in terms of specialized memory structures like the “goal stack.” The goal‐activation model presented here analyzes goal‐directed cognition in terms of the general memory constructs of activation and associative priming. The model embodies three predictive constraints: (1) the interference level, which arises from residual memory for old goals; (1) the strengthening constraint, which makes predictions about time to encode a new goal; and (3) the priming constraint, which makes predictions about the role of cues in retrieving pending goals. These constraints are formulated algebraically and tested through simulation of latency and error data from the Tower of Hanoi, a means‐ends puzzle that depends heavily on suspension and resumption of goals. Implications of the model for understanding intention superiority, postcompletion error, and effects of task interruption are discussed.     

Gesture offers insight into problem‐solving in adults and children

When asked to explain their solutions to a problem, both adults and children gesture as they talk. These gestures at times convey information that is not conveyed in speech and thus reveal thoughts that are distinct from those revealed in speech. In this study, we use the classic Tower of Hanoi puzzle to validate the claim that gesture and speech taken together can reflect the activation of two cognitive strategies within a single response. The Tower of Hanoi is a well‐studied puzzle, known to be most efficiently solved by activating subroutines at theoretically defined choice points. When asked to explain how they solved the Tower of Hanoi puzzle, both adults and children produced significantly more gesture‐speech mismatches—explanations in which speech conveyed one path and gesture another—at these theoretically defined choice points than they produced at non‐choice points. Even when the participants did not solve the problem efficiently, gesture could be used to indicate where the participants were deciding between alternative paths. Gesture can, thus, serve as a useful adjunct to speech when attempting to discover cognitive processes in problem‐solving.     

An effective metacognitive strategy: learning by doing and explaining with a computer‐based Cognitive Tutor

Recent studies have shown that self‐explanation is an effective metacognitive strategy, but how can it be leveraged to improve students' learning in actual classrooms? How do instructional treatments that emphasizes self‐explanation affect students' learning, as compared to other instructional treatments? We investigated whether self‐explanation can be scaffolded effectively in a classroom environment using a Cognitive Tutor, which is intelligent instructional software that supports guided learning by doing. In two classroom experiments, we found that students who explained their steps during problem‐solving practice with a Cognitive Tutor learned with greater understanding compared to students who did not explain steps. The explainers better explained their solutions steps and were more successful on transfer problems. We interpret these results as follows: By engaging in explanation, students acquired better‐integrated visual and verbal declarative knowledge and acquired less shallow procedural knowledge. The research demonstrates that the benefits of self‐explanation can be achieved in a relatively simple computer‐based approach that scales well for classroom use.