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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".