Contemporary news management: managerial functions in journalism©
Bibliographic record
Abstract
In the relatively new frontier of journalism research, many early studies overlooked the significant role of the journalist as a manager of expanding news arenas. Journalism training often neglected to correct the traditional conception of news management that upheld the journalist as a passive figure, easily manipulated by government authorities. As Picard opines, 'curricula have been designed to produce news factory workers who can be dropped into a slot at a journalism factory' [1]. Yet Swanson notes the growing research on computer-mediated communication has been beneficial to the study of a higher form of professional journalism [2]. This article further examines contemporary conceptions of news management involving the journalist as a manager of sources of power within expanded computer networks. The paper conducts a unique case study into online journalists’ news interactions with the first African-American President, Barack Obama, and the first Australian female Prime Minister, Julia Gillard, during their media alliance on fighting terrorism from 2010-2013. Journalists were able to manage informed, active news discussions by emphasizing egalitarian images, inclusive language, and compassion. An effective news environment was characterized by online discussions about issues, rather than personalities, and by journalists’ responsibility for news contributions relating to broader goals in human development.© \n \n[1] R. G. Picard, “Deficient tutelage: Challenges of contemporary journalism education,” in Toward 2020: New Directions in Journalism Education, G. Allen, S. Craft, C. Waddell, and M. L. Young, Eds. Toronto: Ryerson Journalism Research Centre 2015, p. 8. \n[2] G. Swanson, Social Media – Its Impact on Journalism, Communication, and Society in the 21st Century. Naka-ward, Aichi: The International Academic Forum, 2015.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".