THE PEDAGOGICAL EVOLUTION OF REPERTORY GRID TECHNIQUE FOR DIVERSE LEARNING COMMUNITIES: A REVIEW
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.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".