Observer Bias: An Interaction of Temperament Traits with Biases in the Semantic Perception of Lexical Material
Bibliographic record
Abstract
The lexical approach is a method in differential psychology that uses people's estimations of verbal descriptors of human behavior in order to derive the structure of human individuality. The validity of the assumptions of this method about the objectivity of people's estimations is rarely questioned. Meanwhile the social nature of language and the presence of emotionality biases in cognition are well-recognized in psychology. A question remains, however, as to whether such an emotionality-capacities bias is strong enough to affect semantic perception of verbal material. For the lexical approach to be valid as a method of scientific investigations, such biases should not exist in semantic perception of the verbal material that is used by this approach. This article reports on two studies investigating differences between groups contrasted by 12 temperament traits (i.e. by energetic and other capacities, as well as emotionality) in the semantic perception of very general verbal material. Both studies contrasted the groups by a variety of capacities: endurance, lability and emotionality separately in physical, social-verbal and mental aspects of activities. Hypotheses of "background emotionality" and a "projection through capacities" were supported. Non-evaluative criteria for categorization (related to complexity, organization, stability and probability of occurrence of objects) followed the polarity of evaluative criteria, and did not show independence from this polarity. Participants with stronger physical or social endurance gave significantly more positive ratings to a variety of concepts, and participants with faster physical tempo gave more positive ratings to timing-related concepts. The results suggest that people's estimations of lexical material related to human behavior have emotionality, language- and dynamical capacities-related biases and therefore are unreliable. This questions the validity of the lexical approach as a method for the objective study of stable individual differences.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".