Perspectival Understanding of Conceptions and Conceptual Growth in Interaction
Bibliographic record
Abstract
We propose a bridge between cognitive and sociocultural approaches that is anchored on the sociocultural side by distributed cognition and participation, and on the cognitive side by information structures. We interpret information structures as the contents of distributed knowing and interaction in activity systems. Conceptual understanding is considered as achievement of discourse in activity systems, and conceptual growth is change in discourse practice that supports more effective conceptual understanding. We also introduce a concept of perspectival understanding, in which accounts of cognition, including conceptual understanding, include points of view. This concept generalizes the concept of schema by hypothesizing that a perspectival understanding can be constructed by constraint satisfaction when a sufficient schema is not known or recognized. We provide an example in which perspectival understanding was jointly constructed, illustrating an interactional process we call “constructive listening.”
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.038 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| 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".