Organizational factors influencing journalists’ use of user-generated content: A case of Canadian radio newsrooms
Bibliographic record
Abstract
Abstract Using an evaluation framework, this study sheds light on the organizational factors that influence the use of user-generated content (UGC) in radio newsrooms. Although communication research is consistently emphasizing rapid changes in technology and the need for journalists to adapt to their multi-platform environments, this study shows that some radio journalists and managers, at least, are still quite conservative in their innovative practices. Through the use of semi-structured interviews with radio managers and journalists in a major Canadian market, five main uses of UGC were identified in the newsroom. These factors were directly related to the perceived notion of the audience and its value to news reporting. Financial pressures, company policy and professional standards are the three main organizational factors that influence these uses and the extent to which the practices were observed. Age, language and sectorial differences did not appear to be influential in this study. These results, however, suggest that work still needs to be done to establish which factors are most relevant in overcoming fears and challenges with respect to innovative UGC use. A better knowledge of the audience could also influence future behaviour.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.009 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".