Online Journalism: Crowdsourcing, and Media Websites in an Era of Participation
Bibliographic record
Abstract
The era of journalism and the participation of the readers on online media websites have changed online journalism. The research interest is now focused on removing the distinction between the publisher/entrepreneur and the journalist/user, with the ultimate goal of actively involving citizens in the journalistic process but also in the web presence of media websites. The evolution of technology, the deep media crisis and the growing dissatisfaction of the citizens, create the conditions for journalism to work with citizens, and in particular through citizen journalism and journalism crowdsourcing. This concept is a form of collective online activity in which a person or a group of people volunteer to engage in work that always involves mutual benefit to both sides. The main research question of this research concerns the analysis of the current situation regarding crowdsourcing, co-creation and UGC and the adoption of best practices such as crowdcreation, comments from the users, crowdwisdom, instant-messaging applications (MIMs) and crowdvoting used by media websites around the world. Very few media have tried to apply even nowadays, the proposed model of journalism, which this study is going to research. The results of the study shape new perspectives and practices for online journalism and democracy.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| 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".