Verificação de propriedades psicométricas do Inventário de Cristalização das Preferências Profissionais
Bibliographic record
Abstract
Psychometric Properties of the Professional Preferences Crystallization Inventory The Professional Preferences Crystallization Inventory (PPCI) is an important instrument which evaluates two dimensions: choosing a career and thus promoting professional development. This study presents the steps of the elaboration of the PPCI and the measurement of its psychometric properties obtained with a sample of 487 students from a private university in the south of Brazil, of both sexes and age ranging from 17 and 51 ( X = 22; X 5% = 21,3; Median = 20; Mode = 19). The results are presented systematically according to its theoretically proposed dimensions. Alpha coefficients for the two dimensions studied are found to be highly satisfactory (α > 0,85). The results of both the factor analysis and correlations (item-item, item-dimension, and item-total scale) seem to support a two-factor solution. Further studies are still needed to proceed with the investigation of other important psychometric qualities.
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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 | 0.015 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".