Bibliographic record
Abstract
Neighborhoods and whole cities are increasingly being designed with a broadband telecommunications infrastructure that provides access to the Internet and other information and communication technologies (ICTs) (for example CityPlace, Toronto, Canada; Arabianranta, Helsinki, Finland; Kenniswijk, Eindhoven, the Netherlands; and Playa Vista, California, U.S.A.).This access has ignited a debate into the nature of community and the effects of cyberspace on social relationships.On the one side, technological dystopians argue that in an information society where work, leisure and social ties are all maintained from the "smart house," people could completely reject the need for social relationships based on physical location.While on the other side, technological utopians argue that the Internet has created a whole new form of community, the "virtual community," which frees the individual from the restraints of geography and social characteristics like gender, race, and ethnicity.What this "either or debate," arguing community either to be lost or recreated, fails to recognize, is that community has long been freed from geography, and that new ICTs may hold as much promise of reconnecting us to communities of place as they do in liberating us from them.For the most part, "community" still refers to neighborhood.Yet most of the social support, and much of the information and resources that people require to function in their day-to-day lives, comes from sources outside of the local setting (Fischer 1982; Wellman Carrington and Hall 1988).Cities are extremely heterogeneous, residents are highly mobile, and people regularly come in contact with diverse others in a variety of social settings.As suggested by Fischer (1975) in his "subcultural theory," individuals in an urban environment are not limited to those who are close at hand, but seek out social ties based on shared interest and mutual identification.While this does not exclude the possibility that people can form social ties based on shared place, it does suggest that similarity of interest is more important in forming relations than similarity of setting.When one defines communities as sets of informal ties of sociability, support and identity, they are rarely neighborhood solidarities or even densely-knit groups of kin and friends.Communities consist of far-flung kinship, workplace, interest group and neighborhood ties that together form a social network that provides aid, support, social control and links to multiple milieus.Within these personal communities people use multiple methods of communication: direct in-person contact, telephone, postal mail, and more recently fax, email, chats, and email discussion groups.Looking for community in one place at one time (be it in neighborhoods or in cyberspace) is an inadequate means of revealing supportive community relations.Indeed, "community without propinquity" is hardly a new concept, but it is one that is often neglected (Webber 1963).The creation of a whole new type of community, the "virtual community," has done much to highlight the potential for communities to form beyond the confines of geographic space (Rheingold 1993).Technological utopians have found community in cyberspace.Largely anecdotal evidence emphasizes the ability of computer networks to connect people across time and space in strong supportive relationships, blindly extending beyond characteristics of ethnicity, religion or Planning Theory & Practice 3(2), 228-231.2002.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".