Redefining Journalism in the AI Era: Constructing A New Model for Harmonizing AI Technology with Traditional Journalist Ethos and Values
Bibliographic record
Abstract
The development of artificial intelligence (hereafter referred to as AI) has permeated various industrial sectors, significantly transforming organizational dynamics and strategic approaches. The rapid proliferation of information and communication technology (ICT) and the ongoing process of datafication across society have extended their impact to journalism as well (Gelgel, 2020; de-Lima-Santos & Ceron, 2021). The range of AI tools adopted in newsrooms is diverse. AI in journalism is conceptualized as a series of algorithmic processes that produce and disseminate text, images, and videos for public consumption, with minimal human oversight (Carlson, 2015a; Moran & Shaikh, 2022). However, the swift pace of technological advancement has left media companies grappling with confusion. Since the advent of AI, the processes of agenda setting, content gathering and production, and news distribution have radically evolved (Hernandez Serrano et al., 2015; Örnebring, 2010; de-Lima- Santos & Ceron, 2021). These technologies surpass conventional expectations. For instance, Open AI's GPT software series, developed through deep learning, showcases text quality remarkably akin to human writing (Floridi & Christi, 2020; Moran & Shaikh, 2022).
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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 | 0.022 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.008 | 0.069 |
| Scholarly communication | 0.034 | 0.033 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".