Bibliographic record
Abstract
This paper presents an editorial synthesis of the three substantive papers published in this Special Issue. The lens for this synthesis concerns the joint contribution of the three papers in identifying potential bases for explaining variation in Type 2 agency costs of equity in family companies. The papers included in this Special Issue, using data from Portugal and Africa, suggest three bases. These bases are Small-to-Medium Enterprise status, prevalence of third parties to reduce information asymmetry between the principals and agents, and domicile in South Africa (for African family businesses). It follows from the paper using data from Jordan that degree of tax avoidance would be a suitable measure of Type 2 agency costs of equity. Hence, it would be appropriate for future research to investigate whether this metric varies systematically, across family companies, according to these three bases.
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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 | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".