15. The “Innateness Hypothesis” and Explanatory Models in Linguistics

Author(s):  
Hilary Putnam
NASPA Journal ◽  
2005 ◽  
Vol 42 (2) ◽  
Author(s):  
Todd F. Lewis ◽  
Dennis L Thombs

The aim of this study was to conduct a multivariate assessment of college student drinking motivations at a campus with conventional alcohol control policies and enforcement practices, including the establishment and dissemination of alcohol policies and the use of warnings to arouse fear of sanctions. Two explanatory models were compared: perceptions of risk and normative beliefs. An anonymous questionnaire was administered to 1,396 students at a large Midwestern university. Data analyses were conducted on the subsample of participants who had reported using alcohol within the past 12 months (n = 1,322). Overall, the results from a canonical correlation analysis indicated that alcohol involvement was best explained by normative beliefs about the drinking practices of one’s closest friends. Perceptions of drinking risk were less important to the explanation of alcohol involvement, and some of these measures unexpectedly had positive associations with indicators of alcohol risk behavior. The findings call into question the conventional deterrence strategies used in many university communities (i.e., belief that students who perceive there to be a low risk of receiving sanctions are those most likely to engage in alcohol related misbehavior). Furthermore, the findings suggest that effective interventions will need to impact students' normative beliefs about the drinking practices of proximal peer groups.


2008 ◽  
Vol 30 (6) ◽  
pp. 653-672 ◽  
Author(s):  
Mary Lynn Piven ◽  
Ruth A. Anderson ◽  
Cathleen S. Colón-Emeric ◽  
Margarete Sandelowski

Author(s):  
Michal Soffer ◽  
Miri Cohen ◽  
Faisal Azaiza

Abstract Background: ‘Explanatory Models’ (EMs) are frameworks through which individuals and groups understand diseases, are influenced by cultural and religious perceptions of health and illness, and influence both physicians and patients’ behaviors. Aims: To examine the role of EMs of illness (cancer-related perceptions) in physicians’ and laywomen’s behaviors (decision to recommend undergoing regular mammography, adhering to mammography) in the context of a traditional-religious society, that is, the Arab society in Israel. Methods: Two combined samples were drawn: a representative sample of 146 Arab physicians who serve the Arab population and a sample composed of 290 Arab women, aged 50–70 years, representative of the main Arab groups residing in the north and center of Israel (Muslims, Christians) were each randomly sampled (cluster sampling). All respondents completed a closed-ended questionnaire. Results: Women held more cultural cancer-related beliefs and fatalistic beliefs than physicians. Physicians attributed more access barriers to screening as well as fear of radiation to women patients and lower social barriers to screening, compared with the women’s community sample. Higher fatalistic beliefs among women hindered the probability of adherence to mammography; physicians with higher fatalistic beliefs were less likely to recommend mammography. Conclusions: The role of cultural perceptions needs to be particularly emphasized. In addition to understanding the patients’ perceptions of illness, physicians must also reflect on the social, cultural, and psychological factors that shape their decision to recommend undergoing regular mammography.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Naomi A. Arnold ◽  
Raul J. Mondragón ◽  
Richard G. Clegg

AbstractDiscriminating between competing explanatory models as to which is more likely responsible for the growth of a network is a problem of fundamental importance for network science. The rules governing this growth are attributed to mechanisms such as preferential attachment and triangle closure, with a wealth of explanatory models based on these. These models are deliberately simple, commonly with the network growing according to a constant mechanism for its lifetime, to allow for analytical results. We use a likelihood-based framework on artificial data where the network model changes at a known point in time and demonstrate that we can recover the change point from analysis of the network. We then use real datasets and demonstrate how our framework can show the changing importance of network growth mechanisms over time.


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