#Journalism: Twitter’s impact on 21st century journalism practice
Bibliographic record
Abstract
This thesis explores the impact of the hybrid media system on journalism practice in the West. To do this I use a conceptual framework which discusses the normalisation hypothesis in the context of the hybrid media system and considers both homophily =and \ninstitutional logics in an analysis of journalism-audience interactions on the social media platform Twitter. The study explores the question of normalisation through a quantitative analysis of political journalists’ Twitter interactions and two qualitative textual analyses of \nsocial media policies from mainstream news organisations in the US, UK, Ireland, and Canada. This thesis finds that homophily influences journalists’ interactions as they largely use Twitter to focus on each other, a type of practice that typifies “pack journalism” and is \nknown to contribute to groupthink. News organizations are seen to reinforce traditional ideas of professional practice in their guidance which conceptualise the audience as passive, albeit potentially hostile, consumers rather than participants or collaborators and \nthat while they neglect the potential for contributions from their news audiences they also lay down very prescriptive ideas about their employees can and cannot do on social media. \nThese findings suggest that both practitioners and organizations are not only neglecting historic opportunities to create a renewed relationship with their audiences, but that they are also failing to develop proficiency in a system where power resides not just with those who held power in the older media system but also with those who best understand how to work with information in the newer system (Chadwick, 2017). The findings inform the concluding discussion which argues that journalism education needs to consider a hybrid \ncurriculum rooted in academic research and industry practice to better prepare students for a media world of the future
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".