Anspannung: Introduction to concept and quantification of mental strain exemplified on data taken in five countries
Bibliographic record
Abstract
In Dükers action theory Anspannung , which we translated as “psychological tension” (PT), is described as a directly experienced valid indicator for the extent of mental strain. In German-speaking regions the Category Partitioning technique (CP) has proven to be a useful method for accurately quantifying the experienced PT. Outside Germany, however, the concept of PT and the CP technique for measuring it have found little resonance, as it seemed that the central terms could not be meaningfully translated into English. To challenge these language barriers, test the applicability and usefulness of the PT concept, and evaluate the CP scaling method, we used the CP technique to quantify the level of PT required by 32 imagined everyday situations. To do this we adapted descriptions of the everyday situations from the German into English, Japanese, Korean, and Mandarin Chinese, and enrolled N = 158 participants from five countries (Canada, Germany, Japan, South Korea, and Taiwan). The results show a remarkable agreement between the data collected in the five cohorts. The experimental data point to the universality of the experience of mental load in culturally and linguistically diverse societies. They also point to the need to design scaling techniques so that respondents can describe their immediate sensations as they would in everyday life.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".