Measuring Environmental Sensitivity in Educational Contexts: A Validation Study With German-Speaking Students
Bibliographic record
Abstract
Sensory-Processing Sensitivity (SPS), as part of the general theory on Environmental Sensitivity (Pluess, 2015), is a temperamental individual difference variable, referring to sensitive perception and processing of as well as reflection upon environmental stimuli. For its measurement, Aron and Aron (1997) developed the Highly Sensitive Person Scale (HSP Scale) for application with adults. However, despite some adaption into German (Konrad & Herzberg, 2017) and a first English version for children (Pluess et al., 2018), no suitable measures of SPS for children exist in German. The presented two studies aimed at developing and validating a short, 10-item German version of the scale, which can be administered efficiently in educational field studies with German-speaking secondary school students. The factorial structure, its relationship with other personality traits (i.e., the Big Five; McCrae & Costa, 1990) and exploratory analyses on relationships with additional school-related variables were revealed using data from two independent student samples (N = 301 German academic-track secondary school students and N = 460 German vocational track secondary school students). Relations to existing research, practical implications for the educational context, and limitations of the studies are discussed.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".