Intra-firm differentiation of compensation systems: evidence from US high-technology firms
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
While scholars have long recognised the influence of firm decisions on aspects of compensation (e.g. pay level and pay mix), prior compensation studies offer an ambiguous understanding regarding their scope. Some studies argue that firms customise compensation decisions according to employee groups, whereas others assume that firm compensation decisions apply uniformly throughout a firm. To address this research gap, the current study analyses pay levels and pay mixes for R&D employees and administrative employees in US high-technology firms. Our empirical analyses show that firms make distinct compensation decisions for these two job families, but these decisions are ultimately consistent. These findings highlight firms' intention to strike a balance between customising compensation systems according to employee groups and maintaining internal consistency. Our findings add interesting insights to the strategic HRM and talent management literatures, which claim that firms should differentiate among employees when designing HRM systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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