Social network analysis as a means of exploring “users”
Bibliographic record
Abstract
At the 2003 ASIST Annual Conference, a panel called “Death of the User” suggested that the current state/paradigm of user studies is not sufficiently robust as a framework for studying the dynamics of information use. The panel put forth a few suggestions for future frameworks, among them, one in the Communications area that has been receiving some attention in information science lately: social network analysis. The intention of this panel is to (a) continue the dialog begun by the “Death of the User” panel, and to (b) shed light on this means of investigation and how it could be used to better understand how people seek and use information. This method is in contrast to “traditional” user studies in which individual users are usually studied divorced from their social context. Social Network Analysis places the information seeker within his or her social network and seeks to explain how the constraints and opportunities afforded by social networks affect information behavior in individuals' everyday and professional lives and the flow of communication and information within and across organizations. Panelists will provide insights on the strengths and limitations of using SNA to study human use of information. Most research in information science has taken an individualistic approach and people are seen as socially disembodied beings without roles in social Systems (Haythornthwaite & Wellman, 1998). However, people have different roles in their social networks and each role requires a different set of behaviors (Coser, 1975). In this study, different roles in the information network are identified based on social network analysis measures. Six measures of centrality are discussed and compared. Furthermore, the value of these social roles for information seeking is examined. Describes how users may be described in their relations with others, including creating profiles of user based on the kinds of information they give and receive from others, and the network of relations they deal with; examples from online learners, and from science research teams. This presentation will describe work with the homeless and information seeking, which most do via social networks that are sparse and unconnected. Using social network data and interviews conducted in Ulaanbaatar, Mongolia, describes how information needs change during the life course and how these changes affect the characteristics of social networks.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".