The Evocative Power of Projective Techniques for the Elicitation of Meaning
Bibliographic record
Abstract
A unique project was undertaken by doctoral and postdoctoral students, and their mentor, from diverse backgrounds in health and social sciences to explore their past experiences as participants in a qualitative research training initiative called EQUIPP (Enhancing Qualitative Understanding of Illness Processes and Prevention). The purpose of the project was to create a symbolic representation of the EQUIPP program through the use of projective techniques. The authors examined the meaning of engaging in qualitative research training through images and conceptual metaphors that were subsequently consolidated thematically and then portrayed in the form of a newly constructed logo that was developed with the assistance of a professional graphic designer. Projective techniques proved to be a powerful, evocative tool for eliciting meaning and translating concrete experiences into visual discourse. In this paper, the authors discuss how projective techniques were operationalized and consider their broad implications for qualitative research.
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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 | 0.076 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".