Introducing the vox populi of the wired world – how 'blogs' are evolving as dynamic web-based social networks
Bibliographic record
Abstract
With the advent of blogs, virtual communities are witnessing new horizons in social networking and democratic participation. This form of social media is attracting netizens from all quarters of the dot com stratosphere. For personal or professional purposes, an increasing number of people are subscribing to this social technology. This paper examines the cultural landscape of blogs and discusses the motives and the motivation behind the varying blog user behaviour. It further evaluates the growing influence of blogs and how corporates and politicians are utilising the media to advance their interests. Legal issues involved in blogging such as online libel, defamation, employment termination (doocing) are dealt with reference to case laws and reported events. It further throws light on what makes a blog successful in the marketplace of blogs – the blogosphere. In a nutshell, the paper evaluates the overall potential of blogs to evolve as highly dynamic web-based 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 | 0.001 | 0.003 |
| 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 | 0.014 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".