Participatory Critical Incident Technique: A Participatory Action Research Approach for Counselling Psychology
Bibliographic record
Abstract
This article provides an overview of the utilization of the participatory critical incident technique (PaCIT), an approach that incorporates participatory action research (PAR) with the critical incident technique (CIT). This method fits with the aims of counselling psychology to bring social justice and action into the forefront of research activities (Canadian Psychological Association, 2009; Kennedy & Arthur, 2014). PaCIT addresses potential limitations of both methods and is a viable research tool for use with marginalized groups and within cross-cultural contexts. Based on a recently completed project with youth in alternative education, we present a theoretical and practical approach for integrating CIT within a PAR framework.
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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 | 0.126 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".