[es] Los conocimientos en trabajo social: elogio del eclecticismo
Bibliographic record
Abstract
La ciencia está en el origen del trabajo social. En efecto, el objetivo que perseguía Mary Richmond al conceptualizar la disciplina de trabajo social consistía en reemplazar las creencias religiosas por un enfoque racional. Así pues, el conocimiento científico se inscribe en el corazón del trabajo social desde el principio. Dicho esto, las grandes teorías son menos útiles en la práctica de una profesión cuya finalidad es ayudar a las personas que viven las situaciones más diversas. Esta es la razón por la cual el trabajo social se construye a partir de una base ecléctica: el trabajador social eficaz toma de un vasto conjunto de nociones, en apariencia dispares, aquellas que mejor se adaptan a la situación. Ahora bien, ¿cómo enseñar este conocimiento práctico a los estudiantes de trabajo social? Este artículo trata de estas cuestiones y propone algunas pistas de reflexión. Science is closely related to social work, from its very origin. In fact, with the conceptualization of social work, Mary Richmond’s objective was to replace religious beliefs by a more rational approach. Thus, right from its beginning, the scientific knowledge was at the core of social work. This being said, grand theories are less appropriate to the practice of social work because this profession deals with persons living in particular situations. That is why social work is built upon an eclectic basis : social worker has to draw from a vast array of notions and theories to borrow bits and pieces which will help to explain and attenuate problem experienced by persons. Now, this practical knowledge can be taught to students in social work? This article deals with these questions and proposes some reflections.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.085 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".