Realizing Vygotsky's program concerning language and thought: tracking knowing (ideas, conceptions, beliefs) in real time
Bibliographic record
Abstract
Educators generally are concerned with testing what learners know by means of written tests, as if knowledge was some intrapsychological thing or state that could be translated and externalized into some interpsychologically available inscription that is a more-or-less accurate approximation of what the person knows. In such endeavors, language is used as a fixed representational means to achieve the interpsychological representation. Carefully conducted design experiments show, however, that knowing and learning are distributed – and therefore contingent processes – across different temporal, spatial, and social scales, which raises questions about the nature of the knowledge exhibited in various situations and forms. In this paper, I problematize the question of what spoken language specifically – and communicative acts more generally – tell us about knowing, learning, and development. I propose a different way of theorizing and analyzing what people think in action, and the ideas and concepts they mobilize. I articulate a set of propositions about knowing, which I exemplify in a careful look at a randomly chosen lecture episode from a university physics course.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.025 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".