On the Polylithic Nature of Family-Owned Businesses: Heterogeneous Responses to – and from – Brazilian Financial Market Reform
Bibliographic record
Abstract
Accompanying the increasing globalization of firms and financial markets has been the growing belief that both firms and their financial markets will benefit from the adoption of improved corporate governance practices, particularly in emerging markets historically dominated by family-owned firms. We provide a more nuanced examination of this belief by advancing a theoretical perspective on the polylithic nature of family-owned firms, and use our typology of family firms to hypothesize why some family firms are more open to improved corporate governance than others. We contextualize our hypotheses using the recent Brazilian financial market reform, which allowed firms to self-select into new trading sub-segments distinguished by the stringency of corporate governance requirements. We offer and test differentiated predictions as to which type of Brazilian family-owned firms is most/least likely to self-select into more stringent trading segments, and with what financial market consequences. We predict and find that Brazilian financial markets do generally reward firms that choose a more stringent stock market segment, but do not anticipate that some firms engage in symbolic vs. substantive adoption of good governance practices. We discuss the implications of our approach and findings for research on corporate governance, family-owned firms, and institutional change in emerging markets.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".