Knowledge co-construction in professional reading group discussions
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.
Bibliographic record
Abstract
Abstract As part of our longitudinal study of TESOL instructors’ engagement with peer-reviewed journal articles in professional reading groups, we examined the processes involved in knowledge co-construction in three group discussions. Audio-recordings of the discussions were analysed using process coding to identify the quality and quantity of the group members’ (n = 18) contributions and the processes of knowledge co-construction. Findings revealed that the group members’ contributions were characterized by 16 different language functions. The most commonly used functions, agreeing, elaborating and sharing experiences, strengthened group rapport and promoted a positive learning environment. All 16 language functions contributed to the processes of introducing, developing, crystallizing, combining, and creating knowledge that stimulated innovative evidence-informed practices. An awareness of the processes of knowledge-co-construction and their potential to address professional learning and development needs may encourage teachers to engage in autonomous reading groups and support them in the creation of innovative next practices.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it