Cost of effort in benchmarking: exploration and applications
Bibliographic record
Abstract
The literature on incentives suggests that the cost of effort is a key determinant of production, as an agent’s utility is dependent on both consumption and the cost of effort. However, the benchmarking literature has neglected to consider the cost of effort in performance improvement, as it primarily focuses on selecting best-practice benchmarks for underperforming agents. This paper aims to bridge these two literatures by examining the cost of effort in benchmarking and its applications. Our approach is a direct extension of the rational inefficiency hypothesis. We use the information of slacks regarding technology to make inference about the cost of effort in benchmarking. Importantly, we show that inference about the cost of effort gives new sights into activity planning, incentive provision, and employee layoffs. Our analysis provides a new explanation for benchmarking failure in business practices. It also contributes to the rational inefficiency hypothesis by revealing that inefficiency can be beneficial in benchmarking since it can be regarded as a form of fringe reimbursement provided to stakeholders to offset the cost of effort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".