Constructing a group distribution from individual distributions.
Bibliographic record
Abstract
A group distribution is a synthesis of a set of individual distributions. To be adequate, a method for creating group distributions should not introduce characteristics that are not present in the individual distributions and preserve those that are present. A method occasionally used is quantile averaging (sometimes called vincentizations), applied generally to response time distributions. However, it is shown here using quantile-quantile plots on empirical response times that this method is inadequate. As shown by Thomas and Ross (1980, Journal of Mathematical Psychology), to solve this problem, quantile averaging can be generalised using an appropriate nonlinear transformation of the data. Here we argue that the correct transformation is the log transform of response times to which the base response time has been removed. Equivalently, the geometric mean of the quantiles can be used. We first propose 4 estimates of the base response times. We next examine empirical data in a same-different task, in a redundant-attribute target detection task and in a visual search task. The results show that this approach is appropriate to construct group distributions. It can be used to aggregate distributions over multiple participants, over multiple sessions of training for a given participant, or both. (PsycINFO Database Record
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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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".