A Socio-Cultural Analysis of Romanian News from the 18th Century to Digital Era
Bibliographic record
Abstract
When Marshall McLuhan wrote in his 1964 book Understanding the Media: The Extensions of Man that ‘the medium is the message’, the Canadian communication theorist described the role that the new technologies of the time – radio and television – played in shaping social interactions, communication, and the media itself. For decades, scholars have researched how radio and television have influenced news content. However, in the early 2000s their attention shifted towards another medium: the digital environment. From blogs to digital media outlets or social media platforms – and more recently, artificial intelligence – the role of the digital environment as ‘the medium’ that shapes information has become the focus of contemporary inquiry in both the social sciences and communication research. In this paper, we conduct a socio-cultural analysis of Romanian news coverage from a reader’s perspective, investigating news discourse as the convergence of cultural trends, societal shifts, and economic and technological developments. This inquiry is grounded on the premise that a diachronic examination of news coverage can reveal how media discourse has evolved over years, reflecting underlying socio-cultural dynamics as well as economic and technological transformations. To explore this, the study analyses a corpus of news articles published in Adevărul, one of Romania’s oldest newspapers.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".