The Effects of Firm Strategy on the Level and Structure of Executive Compensation
Bibliographic record
Abstract
Abstract Executive compensation has attracted considerable attention over the past few decades. However, a review of the literature suggests a need for more empirical research using different theoretical insights. In this paper, using several theoretical perspectives, we add new insights on the determinants of executive compensation. Using data from a sample of Canadian‐based mining firms, we examine and discuss the effects of firm strategy on the level and structure of chief executive officer compensation. Areas for future research are also discussed. Résumé Les dernières décennies ont vu un très grand nombre de recherches dans le domaine de la rémunération des cadres d'entreprise. Toutefois, la révision des ouvrages publis révèle la nécessité de nouvelles recherches empiriques utilisant des approches théoriques différentes. À l'aide de diverses approches théoriques nous présentons ici de nouvelles idées sur les déterminants de la rémunération des cadres. Employant les données tirées d'un chantillon d'entreprises minières canadi‐ennes, nous examinons les effets de la stratégie des entreprises sur l'échelle de rémunération et le niveau de salaire des directeurs généraux. Nous discutons aussi des aspects susceptibles de devenir les sujets de recherches futures.
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 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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".