The ex-pat effect: presence of recent Western immigrants is associated with changes in age at first birth and birth rate in a Maya population from rural Guatemala

Prediction models and associated factors on the fertility behaviors of the floating population in China

The floating population has been growing rapidly in China, and their fertility behaviors do affect urban management and development. Based on the data set of the China Migrants Dynamic Survey in 2016, the logistic regression model and multiple linear regression model were used to explore the related factors of fertility behaviors among the floating populace. The artificial neural network model, the naive Bayes model, and the logistic regression model were used for prediction. The findings showed that age, gender, ethnic, household registration, education level, occupation, duration of residence, scope of migration, housing, economic conditions, and health services all affected the reproductive behavior of the floating population. Among them, the improvement duration of post-migration residence and family economic conditions positively impacted their fertility behavior. Non-agricultural new industry workers with college degrees or above living in first-tier cities were less likely to have children and more likely to delay childbearing. Among the prediction models, both the artificial neural network model and logistic regression model had better prediction effects. Improving the employment and income of new industry workers, and introducing preferential housing policies might improve their probability of bearing children. The artificial neural network and logistic regression model could predict individual fertility behavior and provide a scientific basis for the urban population management.

More voices are always better: Tackling power differentials in knowledge production and access in human biology

OBJECTIVE

Academic human biology seeks to characterize and explain human biocultural variation in terms of adaptations to local environments. Understanding and educating about such variation, if not carried out thoughtfully, can reinforce power asymmetries around who can produce and access the knowledge, and in what ways and places. One of many factors contributing to power inequities in knowledge production and access concerns histories of state-driven colonization, with people(s) dispossessed of land through colonization generally having relatively less power. Because human biologists disproportionately work with communities/sub-populations living in marginal environments, most of which have been moved, dispossessed, and/or reconfigured through colonization, we are prone to reproducing these land-related power imbalances but we are also well-situated to level them.

METHODS

Here, we do three things we hope will move us toward research and teaching practices that recognize and begin to disrupt colonial power inequities in human biology knowledge production and access.

RESULTS

First, after defining terms core to understanding the power matrices at stake, we outline likely benefits to human biologists of using anticolonial approaches. Second, we highlight two frameworks offering anticolonial tools (community-based participatory research and "two-eyed seeing"). Third, we suggest several practical, behavioral changes to make and skills to develop for human biologists looking to shift power balances.

CONCLUSION

We conclude by reflecting on our own positions along the colonially rooted power gradients structuring human biology. We argue that doing so constitutes an essential early step toward creating anticolonial spaces for more ethical and just production, consumption, and application of knowledge.