Implicit Attitudes to Work and Leisure Among North \nAmerican and Irish Individuals: A Preliminary Study
Bibliographic record
Abstract
The current article reports the findings from two preliminary experiments investigating the \nImplicit Association Test (IAT) and the Implicit Relational Association Procedure (IRAP) as \nmeasures of implicit attitudes in the domain of work and leisure among North American and \nIrish individuals. The IAT and IRAP tasks involved responding under time pressure on a \ncomputerized task, with response latency as the dependent variable. The IAT required participants \nto categorize positively or negatively valenced words with stimuli associated with either Work \nor Holidays. The IRAP required that participants confirm or deny that Work and Holidays are \nsimilar or opposite to positively and negatively valenced words. Participants also completed \nan explicit measure consisting of a Likert-based questionnaire. In both Experiments, citizens \nof the United States of America produced performances on the IAT and IRAP that indicated \nmore negative attitudes to work and more positive attitudes to holidays than both Canadian \nand Irish citizens. Responses on the explicit measures did not accord with this overall pattern \nof group differences. The results support the use of the IRAP as a measure of implicit attitudes \nand furthermore the findings appear to be generally consistent with a recent large-scale survey \nof attitudes to work across 23 countries
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".