Bibliographic record
Abstract
The most satisfying and interesting human learning game‐of‐life is probably a conversation where there is a common will among the participants to promote understanding of our world despite possibly large differences in knowledge and experience. Gordon Pask took conversational learning as more than a general metaphor for humanly significant learning. He identified the essential minimal characteristics of the entities and relationships involved and formalised all that into a recursive learning theory of very broad scope. Over the years, Pask and his various System Research associates validated conversation theory by embodying it in a number of (n‐) person‐machine systems (SAKI, CASTE, THOUGHTSTICKER, TDS, etc.), and by doing case studies with various kinds of learners and tutor‐learners learning and teaching through these interfaces. Reviews some interesting aspects of conversation theory, including both its remarkable insights and some limitations. Concludes that there are good reasons for expecting that the implications of Pask’s approach to educational cybernetics will continue to be explored for many years to come.
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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.008 | 0.028 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".