Thinking high but feeling low: An exploratory cluster analysis investigating how implicit and explicit spider fear co-vary
Bibliographic record
Abstract
Research has demonstrated large differences in the degree to which direct and indirect measures predict each other and variables including behavioural approach and attentional bias. We investigated whether individual differences in the co-variance of "implicit" and "explicit" spider fear exist, and whether this covariation exerts an effect on spider fear-related outcomes. One hundred and thirty-two undergraduate students completed direct and indirect measures of spider fear/avoidance, self-report questionnaires of psychopathology, an attentional bias task, and a proxy Behavioural Approach Task. TwoStep cluster analysis using implicit and explicit spider fear as criterion variables resulted in three clusters: (1) low explicit/low implicit; (2) average explicit/high implicit; and (3) high explicit/low implicit. Clusters with higher explicit fear demonstrated greater disgust propensity and sensitivity and less willingness to approach a spider. No differences between clusters emerged on anticipatory approach anxiety or attentional bias. We discuss results in terms of dual-systems and cognitive-behavioural models of fear.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".