A critical evaluation of subjective ratings: Unacquainted observers can reliably assess certain personality traits
Bibliographic record
Abstract
Abstract Methods to measure consistent individual differences in behavior (i.e. animal personality) fall into two categories, subjective ratings and behavioral codings. Ratings are seldom used despite being potentially more efficient than codings. One potential limitation for the use of ratings is that it is assumed that long-term observers or experts in the field are required to score individuals. This can be problematic in many cases, especially for long-term ecological studies where there is high turnover in personnel. We tested whether raters who were unacquainted with subjects could produce reliable and valid personality assessments of yellow-bellied marmots Marmota flaviventris. Two raters, previously unacquainted with individuals and marmot behavior, scored 130 subjects on fifteen different adjectives in both open-field (OF) and mirror image stimulation (MIS) trials. Eight OF and nine MIS adjectives were reliable as indicated by both a high degree of intra-observer and inter-observer reliability. Additionally, some ratings were externally valid, correlating with behavioral codings. Our data suggest that activity/exploration and sociability can be a reliable and valid measurement of personality traits in studies where raters were unacquainted with subjects. These traits are observable with the personality tests we used; otherwise researchers using unacquainted raters should be cautious in the tests they employ.
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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 | 0.031 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".