Broader Issues in Test Translation and Validation: A Commentary Inspired by Macina et al. (2023)
Bibliographic record
Abstract
Macina et al. (2023) recently reported mixed results on the German translation of the Self and Interpersonal Functioning Scale (SIFS). By focusing on suboptimal indices of structural validity, they recommended choosing other available instruments over the SIFS in future research on personality impairment. Reflecting on Macina et al.'s overall conclusions inspired us to consider broader issues in the field of personality impairment assessment. In this commentary, we discuss some issues regarding test translation and validity raised by Macina et al.'s article. We advise against assuming equivalence between original and translated versions of a test and discuss some caveats regarding comparison between different instruments based on structural validity. We also call into question whether the latter should be the litmus test for judging the quality of a measure. Finally, we discuss how the proliferation of personality impairment measures can benefit the broader field. Notably, this would allow moving toward a "what works for whom" approach that considers the match between psychometric property, desired use of the instrument, and characteristics of the target population.
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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 | 0.344 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.070 | 0.086 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".