REMUNERAÇÃO EXECUTIVA E DESEMPENHOEMPRESARIAL: EVIDÊNCIAS DO "GUIA EXAME - 100 MELHORES EMPRESAS PARAVOCÊ TRABALHAR''
Bibliographic record
Abstract
The paper estimates the brazilian CEO’s elasticity of pay with respect to firm performance, measured by revenues, employing an yet unexplored source of longitudinal dataset on those type of studies: “Guia Exame 100 Melhores Empresas para Voce Trabalhar”, from 1999 up to 2002. The available empirical evidence, however, posits a trade-off between its ineditism and the necessity of solving an aggregative question: to estimate parameters from individual equations when someone has at his/her disposal only averages of variables of interest, i.e., there is a problem of averaged data (see, MACHADO and SILVA (2006)). We estimate a significant elasticity of pay for Brazilian executives (0.16). Such value is comparable to other figures obtained from different analysis (see, MAKINEN (2005) and ZHOU (2000)): higher than those figures from Switzerland (0.11), Germany (0.13) and France (0.13) and lower than those values from Netherlands (0.17), Canada (0.25) and United States (0.28). Our results contribute for a better understanding of the internal labor market of Brazilian CEO’s.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".