Compensation Benchmarking Practices in Large U.S. Local Governments
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
Growing competition over human capital has reiterated the importance of strategic practices to maintaining a high-quality public sector workforce. But how often does the public sector study pay and benefits among competitive peers? This study presents the findings of a national survey of human resource professionals regarding compensation benchmarking practices. Just over half of respondents indicated they conducted a benchmarking study within the last decade. A majority said their jurisdiction only compares compensation with other public employers, with a smaller number including both public and private competitors. Salaries were the most frequent topic of concern; fringe benefits and paid leave time were less often compared. Several jurisdictions conducted benchmarking studies for purposes other than compensation; about one quarter gathered data for purely informational purposes and 9% carried out a study in anticipation of labor negotiations. A series of best practices for benchmarking studies is offered in conclusion.
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.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it