Bibliographic record
Abstract
Humans are both brilliant and idiotic, write Steven Sloman and Phillip Fernbach at the beginning of the Knowledge Illusion. Having glanced at the newspaper before sitting down to write this, where a tribute to the late, great novelist, Toni Morrison, appears alongside an article about Boris Johnson's Brexit "plans", this seemed all too obvious. Sloman and Fernbach themselves concede that, for the most part, their book is simply stating the obvious: we don't know as much as we think we do; we mistake the knowledge of others for our own; our reasoning is primarily causal, but our causal models are shallow and often wrong. Nevertheless, like many ideas, they say, these ones seem obvious only because we've been made to think about them. When we don't think about them which is to say, most of the time we fall prey continually to these "obvious" errors. Most of these errors are largely inconsequential discovering we don't really know how a zip, a flush toilet or a bicycle work won't stop us from using them effectively (although it's a bit more of a problem when we need to fix those that are broken) but sometimes the consequences are dire (nuclear testing accidents, plane crashes, and anti-vaccination campaigns all feature). So, what to do? Sloman and Fernbach don't pretend to have all the answers, but their exploration of our cognitive shortcomings, where they come from, and why they matter is thoughtful, provocative
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".