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Information Networks in Amenity Transition Communities: A Comparative Case Study

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Abstract

Amenity transition, a major socio demographic trend in areas rich in natural resources, is characterized by economic and population growth as a result of retirement in-migration, increased rates of second home ownership, and increases in the number of industries that do not need to be proximate to a specific geographic location. Amenity transition is also characterized by increased intra-community conflict between long-term residents and in-migrants. This research analyzes whether the population growth accompanying amenity transition is associated with variations in the structure and characteristics of intra-community informational networks, as sociological theory would suggest. Methodologically, this is accomplished through a comparative analysis of the structure and characteristics of informational networks in three communities undergoing amenity transition. The analyses suggest population density is not related to either the structure of informational networks or the concentration of trust/distrust within them. When considered in conjunction with previous empirical work, these findings suggest the conflicts associated with amenity transition are more likely to arise because of conflicting value systems and ideologies as opposed to social structural changes in the communities themselves.

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Notes

  1. The word “structural” throughout this article simply refers to the relational nature of all social interactions, whether they be between two individuals, or between and individual and an organization (Entwisle et al. 2007).

  2. However, Toennies argued that both the quantitative and qualitative dimensions of social ties did not co-vary While several studies support Toennies’ argument (e.g., Putnam 1993), consistent and definitive support is lacking (Brint 2001).

  3. This measure of intensity is constructed from individuals’ responses to questions about whether people in their network are “especially close”, “somewhat close”, or “total strangers” (Marsden 1987).

  4. I defined economic dependence as having at least 10 % employment in natural resource based industries (farming, forestry, fishing, hunting, and mining) at one point in time from 1970 to 2010.

  5. This is especially true in small cities where potential sources of data are censored to avoid disclosure pertaining to a specific organizations or individuals (US Census Bureau 2012a).

  6. Prior to 2001, the SIC system aggregates all service industry jobs. Since 2001, the newer NAICS codes separate service industry jobs into the following categories: arts, entertainment, and recreation related employment; employment in accommodation and food services; professional and technical employment; management jobs; employment in administration, waste, and remediation services; educational service related employment; and employment in health care services. I sum the counts across all these industries to match them with the pre-2001 SIC-based data.

  7. As opposed to representing “whole-networks” by collecting data for each relationship within a bounded social group, and which can be used to describe the structure of that group.

  8. The density value calculated from source-by-source matrix would undoubtedly be 1 given each unique pair of informational sources is likely to be used by at least one respondent.

  9. It would also require the analyst to make arbitrary distinctions as to what is and is not a potential informational source. For example, should only formal entities such as civic and religious groups be analyzed even though a vast majority of information flows through interpersonal connections? The approach adopted in this paper circumvents this limitation by simultaneously analyzing formal and informal sources of information. While the large majority of affiliation network studies evaluate individuals’ membership in formal groups such as clubs (e.g., McPherson and Smith-Lovin 1986, 1987), a substantial literature also focuses on less well-bounded patterns of co-affiliation such as participation in online forums or message boards (e.g., Allatta 2003). As Feld (1981:1017) notes “the nature of relations to objects vary; yet they are abstractly similar in that they may be considered relations to generally defined foci; and they therefore have similar implications for group structure.”

  10. I make the assumption that all informational sources are available to all respondents.

  11. While this one-mode matrix can subsequently be analyzed using typical network analytic techniques (so long as they don’t require binary data), I felt it would be unfruitful given the relatively small number of potential informational sources.

  12. This process is identical to analysis of network mobility where affiliational membership is compared across years.

  13. It is possible absence of substantial variation across the datasets is attributable, at least in part, to the simple bipartite nature of the data. More revealing variations might be seen if whole-network data were available within each community.

  14. Pearson χ 2 tests across the three study communities revealed the observed frequency of individuals either distrusting, sometimes trusting, or always trusting each source of information was not significantly different than expected frequencies (df = 4, χ 2 ≤ 9.390, p ≥ 0.052).

  15. Of course, my data are drawn from comparative case studies and the findings cannot be generalized to all communities undergoing some form of community transition.

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Smith, J.W. Information Networks in Amenity Transition Communities: A Comparative Case Study. Hum Ecol 41, 885–903 (2013). https://doi.org/10.1007/s10745-013-9595-7

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