The Post-Rationalist Projective Reactive, PRPR: Validation of the First Post-Rationalist Projective Test
Bibliographic record
Abstract
Starting from the epistemology of complexity and subjectivity, a new instrument has been devised to investigate the subjective ways in which experiences are self referred at the level of immediate experience. The construction and validation process of the Post-Rationalist Projective Reactive (PRPR) are described, the first cognitive post-rationalist projective tool. It investigates Personal Meaning Organizations (PMOs) according to the adaptive and evolutionary post-rationalist approach. Using 20 stimulus tables, it allows respondents to go deeper into the specific narrative plots of their personality and to understand their inner film, to focus on their subjective feelings. PRPR was administered to a cohort of 294 subjects. A Reliability Analysis and a Cluster Analysis were carried out. Cluster analysis identified four clusters that overlapped with the theoretical PMOs description. PRPR pictures showed good internal consistency as Reliability Analysis. Significant convergent validity correlations were found with the results of the Mini Questionnaire of Personal Organization (MQPO; r = .71, p < .001) and of clinical interviews (r = .76, p < .001). The PRPR proved to be both a valuable support for the PMO construct and an effective assessment tool capable of delving into the processes of subjective meaning construction to understand the cognitive organizational closure, reducing the distance between immediate experience and its explanations.
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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 | 0.021 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".