Procesos de intervención el campo de intervención en economía social y estrategias de acción
Bibliographic record
Abstract
Este artículo presenta la economía social como campo de intervención en el trabajo social. La primera parte intenta\ndemostrar la pertinencia de la economía social como una manera de oponerse a la exclusión y promover un modelo\nalternativo de desarrollo. La segunda parte da una explicación del desarrollo de este campo de intervención a partir de tres\nfenómenos: La reestructuración económica mundial, los límites del paternalismo para oponerse a la pobreza y las\ninnovaciones de los movimientos sociales. La tercera parte identifica las principales estrategias de intervención utilizadas\nen economías social: La persuasión, el examen del contexto local, la elección del proyecto, la educación de los promotores,\nla movilización de recursos, la creación de un organismo de apoyo, la institucionalización, la cooperación en conflictos,\nla regeneración y la evaluación.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".