A Clash Of Cultures: The Integration Of User-Generated Content Within Professional Journalistic Frameworks At British Newspaper Websites
Bibliographic record
Abstract
This study examines how national UK newspaper websites are integrating user-generated content (UGC). A survey quantifying the adoption of UGC by mainstream news organisations showed a dramatic increase in the opportunities for contributions from readers. In-depth interviews with senior news executives revealed this expansion is taking place despite residual doubts about the editorial and commercial value of material from the public. The study identified a shift towards the use of moderation due to editors' persistent concerns about reputation, trust, and legal liabilities, indicating that UK newspaper websites are adopting a traditional gate-keeping role towards UGC. The findings suggest a gate-keeping approach may offer a model for the integration of UGC, with professional news organisations providing editorial structures to bring different voices into their news reporting, filtering and aggregating UGC in ways they believe to be useful and valuable to their audience. While this research looked at UGC initiatives in the context of the UK newspaper industry, it has broad relevance as professional journalists tend to share a similar set of norms. The British experience offers valuable lessons for news executives making their first forays into this area and for academics studying the field of participatory journalism. This record was migrated from the OpenDepot repository service in June, 2017 before shutting down.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.023 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".