Bibliographic record
Abstract
Every major newspaper in Nigeria has an online version. It is especially crucial to update and break news. Disqus is an app which provides a veritable platform for readers of these online newspapers to engage and discuss any news story. Disqus emerged through the increasing use of virtual world technologies that act as platforms for end-users to create, develop, and interact, expanding the realm of human communication, interaction, and creativity. Not only researchers and scholars are experiencing the importance of this new field, but also the news industry is strongly investing in these domains, and importantly too, society is responding with huge impacts and transformations through activities of online communities. Online communities are among the most obvious manifestations of social networks based on new media technology and Disqus stands out as a community of contestations; these discussions present layers of interactivity and entanglement which provide counterforces of structures that resonate as evolving communities. Interestingly, these are presented in sheer dramatic episodes sometimes with cause and effect. In other words, presentations occur as dialogues with occasional directions provided by readers. Suffice to add that they read as digital performances. For the newspapers and by-liners these might aggregate as feedback. However, these readers as commenters are at times prone to textual violence, internet bullying and hate speech. These actions and situations prove unsafe for journalism since the readers have pseudonyms or avatars or monikers which encourage anonymity. This research employs elements of textual and discourse analyses to articulate selected feedback and discussions from selected newspapers and saliently provides critical perspectives from these entanglements. The research further utilizes dramatic models like characterization/character development and script analysis to study the readers and their discussions. It conceives these interactions as not only virtual intersections but as virtual convergences. It qualifies some of these engagements as repulsive and outright displays of intolerance. The research duly appreciates that the newspapers which have gatekeepers on these virtual platforms likely show agreeable levels of moderation by commenters in language and interactions. The research submits that even where anonymity prevents readers’ identities, the presence of moderators is critical in providing some safety and this is evidential in some newspapers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".