‘Hermanos, pero no Tanto': the impacts of higher education on journalism careers in Argentina and Brazil
Bibliographic record
Abstract
This article focuses on the impact of journalism education on the careers of news workers in Argentina and Brazil. Through interviews with 40 journalists from both countries, this study addresses this issue by exploring and comparing how different educational backgrounds shape the trajectories of these journalists during their professional transition. The results show that respondents from both countries share a critical view of the ability of higher education programmes to meet the needs of the media industry. However, they disagree on the role and relevance of their educational background. While, Argentine journalists value the practical skills they learned, especially those who studied in technical schools most Brazilian respondents emphasized the critical thinking acquired during their time at university and their internship experiences. Data suggest that the Brazilian training system has favoured the emergence of a clear career pattern: newcomers should first attend a journalism programme, then do one or more internships in order to be hired. Such a pattern is an important mechanism of predictability in journalism careers. In Argentina, the multiplicity of paths and the informal methods used by newsrooms to hire journalists seem to reflect the more precarious situation of the media industry.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".