Networked: The New Social Operating System by Lee Rainie and Barry Wellman
Bibliographic record
Abstract
Lee Rainie and Barry Wellman are two outstanding researchers on the topics of the internet and the social changes triggered by information and communication technologies (ICT). The work carried out by Rainie at the Pew Research Center and by Wellman at the University of Toronto is well known in academic circles concerned with the network society. Those who are exposed to, and closely follow, the changes arising from the network society may know these two authors particularly well, since they are indeed both generators and analysts of new trends in cyberspace. Around 60 books which include in their title the word ‘networked’ appeared in the Library of Congress Online Catalogue for 2012. It is probably not in the process of concept coinage that the authentic novelty of Networked: The New Social Operating System lies. The concept itself can be traced back, through previous work by these same authors to the widely read books by Manuel Castell. Yet, in my opinion, the way in which Rainie and Wellman make use of a quasi-fictional narrative to analyse the network society is truly innovative. Starting with how, in practice, certain characters experience all aspects of their lives within the network society—a truly transitional process which turns them into actual networked individuals—we can observe how a new type of sociability is thus generated from the multiple weak links which are possible thanks to the spread of ICT.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".