Magnitude comparison extended: How lack of knowledge informs comparative judgments under uncertainty.
Bibliographic record
Abstract
How do people compare quantitative attributes of real-world objects? (e.g., Which country has the higher per capita GDP, Mauritania or Nepal?). The research literature on this question is divided: Although researchers in the 1970s and 1980s assumed that a 2-stage magnitude comparison process underlies these types of judgments (Banks, 1977), more recent approaches emphasize the role of probabilistic cues and simple heuristics (Gigerenzer, Todd, & The ABC Research Group, 1999). In this article, we review the magnitude comparison literature and propose a framework for magnitude comparison under uncertainty (MaC). Predictions from this framework were tested in a choice context involving one recognized and one unrecognized object, and were contrasted with those based on the recognition heuristic (Goldstein & Gigerenzer, 2002). This was done in 2 paired-comparison studies. In both, participants were timed as they decided which of 2 countries had the higher per capita gross domestic product (GDP). Consistent with the MaC account, we found that response times (RTs) displayed a classic symbolic distance effect: RTs were inversely related to the difference between the subjective per capita GDPs of the compared countries. Furthermore, choice of the recognized country became more frequent as subjective difference increased. These results indicate that the magnitude comparison process extends to choice contexts that have previously been associated only with cue-based strategies. We end by discussing how several findings reported in the recent heuristics literature relate to the MaC framework.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".