Citizen videojournalism in professional online news media as a vehicle for democracy
Bibliographic record
Abstract
"Citizen journalists have reconstructed the traditional means of journalism practice by being their own eyewitness reporters, producers, and news information distributors; they self-advocate for citizens' voices, analyze news, debate, and construct news stories from citizens' perspectives. The internet has created an open system that encourages free press and enables the mobilization of the rights and practice of public free speech. Deliberative democratic goals are significant in participatory journalism, and these challenges mainstream news journalism in their traditional roles as conveyors of journalism standards, professional practices, and ethics mediation. As new media's momentum picks up, the journalistic space between public and professional journalists will need to be shared, and the practices of journalism will have to shift their models for this new form of democratic platform. Through new media and new journalism practices that encourage citizen involvement, journalism is evolving in setting a different standard of what is newsworthy. It is shifting editorial and political agendas to make use of wider content from the public, which could possibly infer the approval of using new journalism models to encourage citizen participation in news making. This research examines how the democratic practice of citizen participation in news content submissions affect news standards, by which the quality of news journalism is evaluated. This paper assesses how news organizations obtain content from citizens, how they make decisions to print and broadcast the content, and asks whether journalism has progressed into a model that involves citizens and professionals in an effective news production process. This study focuses on the implication of acquiring contributions of citizen journalism from the distinctive perspectives of news practices, participatory journalism, and the deliberation of democracy of citizens' press. This paper focuses on how the integration of user-generated content (UGC), in news stories as a vehicle that provides voices for citizens in a democratic movement. The usage of UGC in news institutions has changed traditional journalistic practices in terms of their news value, standard and quality. Essentially, editors of the news media determine which images captured by citizen journalists will be used, and they also decide the messages that they want to send to the public through framing and editing techniques. Previous research on editorial practice has identified various standards about newsworthiness that serve as selection criteria. However, there is a limited amount of research available on how UGC has changed the traditional journalism model. Through qualitative interviews with seven image editors, five news companies, The Canadian Broadcasting Corporation, CTV, Macleans, Rabble and The Toronto Star, this study sheds light on the editor's perceptions about citizen journalist videos and images in news content online. Three Canadian case examples are examined for their visual content analysis: the immigration ex-judge sex bribe case, the Vancouver police tasering of a Polish man, and Victoria police manhandling two young men at a nightclub. Through analysis of the interviews and case studies, this study finds that editors feel that UGC has not altered their traditional news standards. However, upon closer examination of news report cases, it does appear that UGC, which often consists of low quality videos with information entertainment content, has in fact affected the practices of quality journalism. The news media have adopted UGC content styles, which tend toward being more sensational, graphic, raw; these styles can make "hard news", which conveys investigative in-depth information, appear similar to "soft news", such as sensational infotainment. Notwithstanding that professional news organizations use public content in their news stories, they have not provided a platform of partnership to allow citizens to have a democratic voice through their media"--From Abstract.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".