Bibliographic record
Abstract
Se presentan dos modelos conceptuales quehemos desarrollado para comprender las formasen que los medios sociales pueden apoyarel aprendizaje. Uno se relaciona con el aspecto“social”, que describe las distintas maneras enque las personas pueden aprender con otras yunas de otras en una o varias de tres formassociales: grupos, redes y conjuntos. El otromodelo son “medios” y describe cómo se construyenlas tecnologías y los roles que desempeñala gente en la creación y representaciónde éstos, tratándolos en términos de lo blandoy lo duro que pueden ser. Ambos modelos soncomplementarios: ninguno proporciona unaimagen completa pero, de forma conjunta, ayudana explicar cómo y por qué los distintos usosde los medios sociales tienen éxito o fracasan.Por último, se ofrecen algunas sugerencias encuanto a cómo los medios utilizados para apoyardistintas formas sociales pueden ablandarse oendurecerse para una aplicación más efectiva.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".