KNOWLEDGE CREATION AND COLLECTIVE MEMORY IN THE ERA OF WEB 2.0 AND NETWORK NON-NEUTRALITY
Bibliographic record
Abstract
This paper examines knowledge creation and ownership issues raised by the nexus of Web 2.0 technologies and business models, changes to network neutrality policy and operation, and the advent of next generation Internet architectures. Context One constant in the production of human knowledge is the evolution of modalities of knowledge representation and knowledge sharing. At certain transitions, existing societal norms regarding these modalities have been challenged at fundamental levels. Notable examples can be seen in the area of communication. For example, d'Arcy (1969) derived the influential concept of a right to communicate in the 1960s out of a recognition that newer technologies – satellite-based communication at that time – had raised issues that were not easily addressable by existing norms. 1 The Internet has itself been a long, steady fount of changing perspectives not only on communication issues, but also the relatively less well-examined process of managing externalized knowledge. A nexus of recent engineering developments and changing business practices, known commonly as Web 2.0, is producing socio-technical shifts in the loci and control of both individual and collective representations of knowledge. New web application architectures are encouraging the caching of ever increasing
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.013 | 0.028 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".