Bibliographic record
Abstract
La tecnoética (te)es un campo de investigación interdisciplinar en el que se estudian los aspectos éitcos y morales de la tecnología en la sociead (Mitcham, 2005; Luppicini, 2008). El propósito de este artículo es promover una reflexión alrededor de los siguientes interrogantes: ¿Qué lugar ocupa la tecnoética en las sociedades de la información y la comunicación? ¿Cuáles son los desafíos de la comunicación en cuanto a ética y responsabilidad social? El artículo aporta una reflexión teórica sobre el papel que desempeña la tecnoética en las sociedades de la información y la comunicación. Para ello, analiza la relación que existe entre técnica y ética con el propósito de comprender mejor los problemas y las posiblidades de las organizaciones y la sociedad en cuanto a ética y responsabilidad social.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".