Historias de Vida y Teorías de la Educación: Tendiendo Puentes
Bibliographic record
Abstract
In this paper I discuss life history approaches as research methodology, as educational tools, and as testimony. The discussion is placed within the context of economic globalisation and epistemological reshaping during the last few decades. Firstly, I analyze the research dimension of biographical perspectives. Secondly I point out the educational and testimonial dimensions of life histories in different settings such as teacher preparation and professional development, lifelong learning, genealogical and family work, educational auto/biography in a university context, historical memory, and popular education. Thirdly, I discuss some trends in educational theory and I outline some contributions of life history approaches to an educational theory grounded on critique and resistance.This paper offers a European perspective based on a review of relevant literature in English, French, Italian, and Spanish.
 Key words: life history, narrative, qualitative methodology, remembering, experiential learning, emotional education
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".