La Teoria de la Educación: Una Búsqueda sin Término en la Construcción del Conocimiento de la Educación
Bibliographic record
Abstract
Knowledge of educational issues is construed using very different sources which, in turn, produce different forms of knowledge. Theory of education constitutes a fundamental source in the building of knowledge of educational matters. This essay makes the case that theory of education plays a special role in educational research and it shows how educational theory plays a specific role by enabling us to approach educational problems with our own criteria. Further, educational theory has both epistemological and ontological bases, the former referring to the way of knowing and the latter to the reality it studies. Thus, it sustains education making it susceptible to be studied with functional autonomy, using and developing the reality that is education as an object and a goal. Here, the author argues that educational theory faces the challenge of remaining open to research to favour its development as a scientific construction and academic discipline, in the form of either research about education as part of reality, or research into the knowledge of education. Key words: education, educational Theory, globalisation, knowledge of education, research paradigms
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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.094 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".