The Reliability of Child-Friendly Race-Attitude Implicit Association Tests
Bibliographic record
Abstract
Implicit attitudes are evaluations that are made automatically, unconsciously, unintentionally, or without conscious and deliberative processing (Nosek et al., 2007; Gawronski and De Houwer, 2014). For the last two decades implicit measures have been developed and used to assess people's attitudes and social cognition, with the most widely used measure being the Implicit Association Test (IAT; Greenwald et al., 2003). This measure has been used extensively to assess racial biases and a number of studies have examined the reliability of the IAT when administered to adults (Cunningham et al., 2001; Gawronski, 2002; Greenwald et al., 2003; Nosek et al., 2005; Nosek and Smyth, 2007; Bar-Anan and Nosek, 2014). In recent years, the IAT has also been modified for use with children. Despite the potential of this measure to provide insight into the early emergence of implicit racial attitudes, little is known about the psychometric properties of these modified child-friendly IATs (Child-IATs). In the current research we examined the internal consistency of race-attitude Child-IATs when either reduced (Study 1) or traditional-length (Study 2) versions were administered to children (Studies 1 and 2) and adults (Study 2). We also examined the test-retest reliability of this measure with both child and adult participants (Study 2). We found that these measures demonstrate an internal consistency comparable to what has been seen in previous research with adults. In addition, the internal consistency of traditional-length Child-IATs completed in succession depended on the order in which they were completed; the first Child-IAT demonstrated higher internal consistency than the second for both children and adults (Study 2). Finally, we provide the first evidence that the test-retest reliability of the Child-IAT is comparable to what has been found previously with adults (Study 2). The implications of these findings for future research examining children's implicit social cognition are discussed.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".