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Record W2593720398

“Good Moves” in knowledge-creating dialogue

2016· article· en· W2593720398 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueQwerty · 2016
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDialogicConstructiveSchema (genetic algorithms)Computer scienceFocus (optics)Bridging (networking)Knowledge managementEpistemologySociologyPedagogyProcess (computing)
DOInot available

Abstract

fetched live from OpenAlex

Dialogue is the mainstay of idea-centered education at all levels and is especially important in generating new knowledge or improving existing knowledge. This paper outlines types of “good moves” in knowledgebuilding dialogue – that is, constructive dialogic actions that can contribute to attaining goals of such dialogue – to solve problems, resolve disagreements, generate innovations, new concepts and conceptual structures. The focus of the discourse moves schema is on knowledge creation, with critical thinking entering as an important adjunct. Although only functional elements of knowledge-building dialogue are indicated, not more detailed processes, the scheme is applicable to the design of technological supports for such dialogue and for acquainting students with this kind of dialogue and the competencies that need to go with it.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.628
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.046
GPT teacher head0.390
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it