Some thoughts on essence placeholders, interactionism, and heritability: Reply to Haslam (2011) and Turkheimer (2011).
Bibliographic record
Abstract
In the target article (Dar-Nimrod & Heine, 2011), we provided a social-cognitive framework which identified genetic essentialist biases and their implications. In their commentaries, Haslam (2011) and Turkheimer (2011) indicated their general agreement with this framework but highlighted some important points for consideration. Haslam suggested that neuroessentialism is a comparable kind of essentialist bias and identified similarities with the genetic essentialism framework. In response, we acknowledge similarities but also identify qualitative and quantitative differences between genetic essentialism and other kinds of essentialist biases. Turkheimer challenged us to extend our discussion to address the question of how should people respond to genetic etiological information, critiqued the use of heritability coefficients, and identified a new construct (1 - rMZ), which may be termed a free-will coefficient. In response, we emphasize the need to transform interactionist explanations from being empty platitudes to becoming the default conceptual framework; we wholeheartedly accept his critical view of heritability coefficient estimates (but acknowledge a more limited utility for them); and we are intrigued by his conceptual interest in identifying free-will coefficients yet warn against falling into pitfalls similar to those that were stumbled into in the past.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.054 | 0.073 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".