Decoding effort: Toward a measure – and a better understanding – of effort intensity in accounting research
Why this work is in the frame
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Bibliographic record
Abstract
This study introduces pupillometry – the measurement of pupil diameter changes – as a direct approach to capturing effort intensity in management accounting research. Traditional approaches using self-reports or performance-based proxies have limited researchers’ ability to study how management control systems influence behavior through effort. Using a controlled experiment with a decoding task, we examine how piece-rate versus flat-wage compensation influences effort intensity and performance. Our findings show that pupil dilation partially mediates the relationship between incentives and performance, with this mediation strongest in early experimental rounds before weakening over time. This dynamic pattern suggests that while incentives initially influence performance through effort intensity, other mechanisms such as implicit learning emerge in later rounds. Beyond demonstrating pupillometry’s validity for measuring effort intensity, we highlight its potential applications across management accounting research streams, enabling researchers to better understand how control system elements influence behavior through effort.
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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 | 0.060 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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