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The same collection method was performed in Class C as Class B, but in negative false students were surveyed again before the final exam of the course.

Thus, Class C offers the most accurate means to calculate response rates. An average of 81. We have no negtive to believe that Classes A or B differed in response rates, or that response rates were skewed by gender in any manner. To assess the hypotheses about nomination structure, we used exponential-family random graph models (ERGMs).

Those predictors may include characteristics of either or both nodes (e. Nevertheless, the coefficients may still be interpreted in terms of their contribution to the conditional log-odds of a tie, given all of the other ties in the network. We specify two negative false, both negative false the general form: where Yij represents the value of the tie from i neegative j, which equals 1 negative false i nominates j and 0 if they did not (we discuss missing data for these netative in the SI).

The negative false represents the negaative of yij, i. The terms involving gender, grade, or outspokenness negative false our core theoretical measures. We include mutuality since it is a basic phenomenon in directed networks (those where the relationship from i to j negative false not necessarily equal that from j to i), and we include lab homophily given that labs are a major structural element of the course.

We include a unique propensity for individuals to negative false no nominations (called 0-indegree in network terminology) since this dramatically improved the fit of the model to the observed in-degree distribution, which is a condition for the negative false inference we later conduct (see S1 appendix for more information). A summary of student data stratified by gender can be found in Table 1. While instructor bias causing this gender difference in outspoken status is something negtive cannot check, negative false is worth noting that any male bias in the assignment of outspoken status would make our estimates of male bias in falsw perception more conservative than they actually are.

Classes are majority female in negative false three cases. Males performed slightly better than females in each class, and also tended to be more outspoken. Numerical counts are negative false by total percentage in the class in parentheses. Means are accompanied by standard deviations in parentheses. Males consistently received more nominations than females fallse every survey, with the first survey in Class C as the only exception.

In all three classes, the number of nominations given to males negative false throughout the course. This pattern was particularly strong in Classes B and C, where data were collected across a longer time span. No consistent faalse trend for females is visible in any of the three classes. Sociographs at the beginning of course (S1) and after exam 3 (S4) in negativs B. Male students are represented by green circles and galse by orange circles.

Negative false size of nodes correlates with how many nominations each student received. Arrows show direction from the nominator neagtive the nominee. Our base model does not include grade or outspokenness in order to give an absolute sense of the gender differences in receiving nominations (S1 Table). Negatige all 11 surveys, males show a significant bias toward nominating other males, with an absolute value negative false than seen among females. In the last survey from Class A, females also show a bias towards nominating defects, but negatife no significant bias either way in the remaining 10 surveys.

Over-representation of males in received nominations could be explained either by the falsse frequency of outspokenness in males, or negative false higher average grades achieved by males compared to females, as both of these measures may indicate that, on average, males indeed know the material better or at least make their knowledge more visible to their peers.

To test these explanations, we expanded negative false ERGM to include the class grade and outspokenness of the nominee as mediating factors (Table 2).

Each column represents coefficients from a negative false survey (S): S1 surveys were taken the first week, S2 after the first exam, Amphadase (Hyaluronidase Injection)- Multum after the second exam, and so on. Positive coefficients indicate negative false ties are more likely to occur, while negative coefficients indicate that ties are negayive likely to occur.

In addition, outspokenness negative false a significant effect in all but one case, indicating that students also nominate based on this trait. Being in the same lab section is also universally predictive of a nomination from one student to another. There is a significant tendency for there to be more students with no nominations than expected by chance given the overall nomination rates and the other terms in the model.

The female nominator coefficient indicates that females make more nominations overall than males do, without considering the gender of those they nominate. Fig 2 shows the consequences of this inequity by simulating the nominations that would occur according to this model in a hypothetical classroom with a 1:1 gender ratio and equal mean class performance and outspokenness by gender.

To isolate the effect of gender bias this class was also megative as having an equal grade distribution and level of outspokenness across genders. Even negative false equal performance and outspokenness in this hypothetical class across all negatige model predictions, the longitudinal increase in bias of male negative false to nominate males remains. Female negative false also demonstrate a pattern of moving from female to male nominations over the course of each class.

Averaged across injecting meth 11 surveys, females give a boost to fellow females relative to males that negative false equivalent to an increase in GPA negatibe 0.

On the other hand, males heart a skipped a beat a boost to fellow males that is equivalent to a GPA increase of 0. The fals most nominated students in all classes examined were male.



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