The dialectic between knowledge, knowing, and concept in the theory of objectification
Bibliographic record
Abstract
The theory of objectification (TO) is a theory of learning that seeks to foster conceptually rich, and critical, inclusive, and democratic pedagogical practices. The conception of these practices is based on a new understanding of learning as a cultural-historical process. In turn, the theoretical formulation of learning is anchored in a conception of knowledge that departs from the accounts of rationalists and (new and old) empiricists. The purpose of this article is to offer an overview of knowledge and learning as conceived in the TO. This overview is, of necessity, philosophical, as it addresses a problem that has often been overlooked in educational research: the ontological problem of the nature of knowledge – such as mathematical and scientific knowledge. The philosophical overview presented here is based on a specific philosophy that inspires the theory of objectification: dialectical materialism. Drawing on this philosophy, I theorize learning as a social, embodied, affective, semiotic, and material process where individuals encounter knowledge. In this encounter knowledge manifests itself in sensible practical and material activity through what it is called here knowing and concept. As argued in this article, knowledge, knowing, and concept are three modes of existence of a same entity that is invoked in the movement of learning.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.050 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".