Combining Speed and Accuracy into a Global Measure of Performance
Bibliographic record
Abstract
Response time and accuracy are two of the most frequently collected dependent measures. Tradeoffs between speed and accuracy are often observed, both between people, and between experimental conditions. In this paper we consider how speed, and accuracy, can be combined into a single, overall measure of performance. We consider two different approaches that adjust accuracy scores based on observed speed of responding and we examine how well those measures work with different data sets. We then present a third approach that combines standardized speed and accuracy scores. We show how this latter approach can represent the data fairly well regardless of which (if any) speed-accuracy tradeoff occurs in the data. We also show how this measure can be further generalized by applying differential weightings to the standardized scores of speed, and accuracy, respectively. We conclude by discussing the value of the measure for use in analyzing human performance data where continuous indicators of accuracy or error can be collected or constructed relatively easily. Our goal in developing the global measure of performance is not to accurately model the speed-accuracy relationship, but rather to create a measure that is more sensitive to experimental differences and causal relationships than either speed or accuracy alone.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".