Biases and fallacies, memories and predictions: Comment on Roy, Christenfeld, and McKenzie (2005).
Bibliographic record
Abstract
Comparative theory testing is a useful method for assessing the value of a new theoretical account such as the memory bias account of optimistic time predictions. However, such comparisons can be misleading when they do not carefully consider the domain limitations of the respective theories. M. M. Roy, N. J. S. Christenfeld, and C. R. M. McKenzie have contrasted the memory bias and planning fallacy accounts in their ability to explain the prevalence and degree of optimistic bias in time predictions. However, the authors argue that many of the points of distinction they draw are actually reflections of the domain limitations of the 2 theories. The authors clarify the definition and scope of the planning fallacy account and show how the apparent contradictions diminish or disappear.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.047 | 0.047 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".