From automata to algorithms: A jobs-to-be-done approach to AI in journalism
Bibliographic record
Abstract
Esta exploración del impacto de la inteligencia artificial (IA) en el periodismo establece paralelismos con los autómatas del siglo XVIII, rastreando una fascinación histórica por la vida artificial hasta las tecnologías de IA generativa actuales. Se discute cómo la IA está transformando las prácticas periodísticas, desafiando las nociones tradicionales de la profesión. En lugar de adoptar una posición binaria que enfrenta a humanos contra máquinas, se propone un marco de «tareas pendientes» para entender el papel de la IA en el periodismo. Este enfoque destaca cómo la IA puede servir mejor a las necesidades de las comunidades y las organizaciones mediáticas, alejándose de un enfoque centrado en qué tareas periodísticas pueden o no ser reemplazadas por la IA. Se sugiere un futuro híbrido en el que humanos e IA colaboren en el periodismo, con roles y responsabilidades que evolucionan según el trabajo a realizar para la industria, la profesión y el público
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".