Within-Person Variability in Job Performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although both researchers and practitioners know that an employee’s performance varies over time within a job, this within-person performance variability is not well understood and in fact is often treated as error. In the current paper, we first identify the importance of a within-person approach to job performance and then review several extant theories of within-person performance variability that, despite vastly different foci, converge on the contention that job performance is dynamic rather than static. We compare and contrast the theories along several common metrics and thereby facilitate a discussion of commonalities, differences, and theory elaboration. In so doing, we identify important future research questions on within-person performance variability and methodological challenges in addressing these research questions. Finally, we highlight how the conventional practical implications articulated on the basis of a static, between-person perspective on job performance may need to be modified to account for the dynamic, within-person nature of performance.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it