EPA-0614 – Development and initial structure analysis of a french version of the pathological narcissism inventory.
Bibliographic record
Abstract
The objectives of this study were to develop a French version of the Pathological Narcissism Inventory (PNI ; Pincus et al., 2009) and to examine its internal structure. This French version was developed using a forward-backward procedure. Two two-person teams worked independently, one translating from English to French, the other translating back from French to English afterwards. Next, the two teams were assembled, and a new person joined them, and this committee developed the final French items on the basis of this work. The translated measure was then administrated to French speaking participants and the internal structure of this version was examined through a series of Factor Analyses. First, Confirmatory Factor Analysis (CFA) failed to reproduce the original one-level structure of the English version, as well as the two-level structure identified by Wright et al. (2012). Principal Component Analysis (PCA) was then performed to identify the internal structure of our data, which appears very similar to the English one and two-level structures. The sample was then randomly divided in two and the best fitting model was compared with PCA and CFA in the two subsamples. Finally, a multilevel model with fewer items best fits the data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".