ANALYSIS OF THE SCHOOL PURPOSES OF A SCHOOL LANGUAGE AND COMMUNICATION TEXT FROM CHILE FROM THE LOOK OF THE COGNITIVE VALUE OF THE KNOWLEDGE PROPOSED
Bibliographic record
Abstract
This article proposes a learning content analysis model proposed in a curriculum. It questions the educational purposes of a Chilean language and communication textbook based on Young's (2008; 2009) proposal regarding the cognitive value of knowledge that empowers students. The emancipatory objective of a school curriculum would result from the structuring and organization of said knowledge. What is knowledge that empowers? What are the intellectual activities in which the teaching of this knowledge involves students? In this contribution, we conceptualize knowledge that empowers by referring to the relationships between two processes of generalization of experience, as outlined by Vygotsky (1934/1997) in his discussion of the relationships between everyday concepts and scientific concepts. Although with important limitations, our data analysis highlights that in order to analyze the emancipatory potential of a school curriculum, it is necessary to take into account the way it organizes and structures knowledge. Throughout the article, we show that empowering knowledge is developed through teaching-learning activities that involve the person in a process of theoretical generalization. Finally, we highlight the contradictions between the emancipatory purposes that are announced by its designers and the way in which knowledge is organized and structured in the mother tongue teaching curriculum.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".