Reconsidering “Best Practices” for Testing the Ideal Standards Model: A Response to Eastwick, Finkel, and Simpson (2018)
Bibliographic record
Abstract
Eastwick, Finkel, and Simpson (2018) advanced recommendations for "best practices" in testing the predictive validity of individual differences in the extent to which perceptions of partners match ideal standards (ideal-partner matching). We respond to their article evaluating the strengths and weaknesses of different tests, presenting new analyses of existing data, and setting out conclusions that differ from Eastwick et al. We (a) argue that correlations between ideal standards for attributes in partners and corresponding partner perceptions are relevant to the ideal standards model (ISM), (b) show that important methodological and statistical issues qualify their interpretations of prior research, (c) illustrate a new analytic approach used in the accuracy literature that tests (and controls for) confounds highlighted by Eastwick et al., and (d) provide evidence that the direct-estimation measure of ideal-partner matching is a valid and useful method. We conclude with a cautionary note on the concept of best practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".