THE PEDAGOGICAL EVOLUTION OF REPERTORY GRID TECHNIQUE FOR DIVERSE LEARNING COMMUNITIES: A REVIEW
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
This paper considers the potential for Repertory Grid Triadic Elicitation Technique (RGT) to serve as pedagogical model enabling learner communities across diverse fields to elicit conceptual structures to sustain future-oriented learning that operationalizes a meta-reflection view. The authors survey investigations from fields sharing common challenge: to provide evidence of dimensional change in learner thinking to meet the ever-changing needs of complex post-industrial learning ecosystems. The repertory grid data matrices serve as collective cognitive maps, making explicit some of the tacit knowledge structures which characterize such groups, and support the informal learning community as purposeful, reflective, non-institutional space for knowledge construction. The authors conclude that Conceptual or Repertory Grid elicitation and analysis, founded in Personal Construct Psychology (PCP), helps to develop stronger theoretical foundations for human socio-cognitive activities, particularly when aided by computers as mindtools, thereby contributing to agile knowledge and emancipatory learning across fields and spaces. Keywords: Personal construct theory, repertory grid, pedagogy, professional learning communities, online learning. Cite as: Pidzamecky, U., & vanOostveen, R. (2021). The pedagogical evolution of Repertory Grid Technique for diverse learning communities: A review. Trends in Social Sciences, 3(1), 10-23.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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