Use of Exemplar Surveys to Reveal Implicit Types of Intelligence
Bibliographic record
Abstract
Implicit theories of intelligence were investigated via surveys of exemplars of intelligence. Study 1 was a four-sample survey of famous exemplars. These diverse samples reported a similar set of popular exemplars, which clustered into five groups. These groups represented five types of intelligence: scientific, artistic, entrepreneurial, communicative, and moral intelligence. In Study 2, the minimal overlap of intelligence exemplars with those of fame, creativity, and wisdom refuted the possibility that exemplar reports are indiscriminate or solely a result of availability. In Study 3, knowledgeable judges rated the similarity of 50 famous persons to exemplars representing each type of intelligence. All five similarity ratings predicted exemplar popularities. In Study 4, where exemplar reports were not restricted to famous people, 31% were nonfamous (friends, family members, teachers, etc.). The results indicate that five implicit types of intelligence, each represented by highly available exemplars, play a role in people’s implicit theories.
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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 | 0.009 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".