<p>Introduction: The phenomenological, epistemological, and semiotic components of generalization</p>
Bibliographic record
Abstract
In the first part of this article, I argue that generalization involves three related components: phenomenological, epistemological, and semiotic. I also argue that the concept of generalization conveyed by theories of knowing (e.g., rationalist and empiricist) depends on the manner in which these theories understand the above three components and their interrelations. I elaborate my argument in reference to a cultural-historical dialectical concept of generalization. In the second part of the article, I provide an overview of the articles contained in this special issue and discuss their contributions to educational research.Los componentes fenomenológico, epistemológico y semiótico de la generalizaciónEn la primera parte de este artículo, sostengo que la generalización incluye tres componentes entrelazados: un componente fenomenológico, un componente epistemológico y un componente semiótico. También sostengo que el concepto de generalización que presentan las teorías del conocimiento (por ejemplo, teorías racionalistas y empiristas) depende de la manera en que esas teorías conciben los tres componentes anteriores y sus interrelaciones. Mi argumento es elaborado a partir de un concepto cultural histórico dialéctico de generalización. En la segunda parte del artículo, hago un resumen de los artículos contenidos en este número especial y discuto sus contribuciones a la investigación en educación.Handle: http://hdl.handle.net/10481/34986WOS-ESCINº de citas en WOS (2017): 1 (Citas de 2º orden, 0)Nº de citas en SCOPUS (2017): 1 (Citas de 2º orden, 0)
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".