The Concept of “Web Science” in the Social Realm: Building Bridges between a new Interdisciplinary Field and the Cultural “Wealth of Networks”
Bibliographic record
Abstract
Discourse in the field of “cyber culture” largely does not take into account the major shift in constituent technology that has begun to advance the Web from one based solely on human-understandable hypertext documents to one based on machine-understandable data. Such innovation includes the refinement of new search engine technology to mine data in Web services applications (the “Deep Web”) coupled with the desire to annotate data with mark-up languages that facilitate greater interactivity and infer meaning within either user-created knowledge representation models (“folksonomies” as a part of “Web 2.0”) or more rigid ontological structures (part of the “Semantic Web” or “Web 3.0”). In this paper, I consider this overall evident and predicted shift from a “Web of documents” to a “Web of data” to be the central element in the creation of the next-generation of the Web, and the recent drive to study it within an integrated framework known as “Web Science”. Central to this shift is the need to reconsider not only the cultural aspects of the medium, but also the interactions between cultural theory and technical texts. I conclude that with the emergence of certain new technology the entire concept of intellectual property, and more specifically where value ultimately lies in terms of the creation of cultural product, is also changing. Within, I thus focus on alternative frameworks (namely the work of Yochai Benkler) to conceptualize knowledge production, in order to re-examine issues of Web-enabled participatory culture. In order to highlight new cultural paradigms, opportunities and challenges, I discuss how the concept of “social production” may foster a “cultural democracy” that transcends traditional hegemonic conditions that encumber publics.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.072 |
| Scholarly communication | 0.019 | 0.034 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".