Socioemotional wealth (SEW) across borders: Integrating national context into SEW research
Bibliographic record
Abstract
This study addresses the challenges associated with integrating the national context into socioemotional wealth (SEW) research and highlights the consequences of overlooking contextual variations. We emphasize two critical issues: inadequate testing of SEW assumptions and threats to the construct validity of SEW measurement. We recommend that cultural and institutional aspects of the national context should be incorporated to understand how family owners prioritize SEW dimensions, and how their willingness trades off current SEW wealth for prospective financial gains. We also conduct an exploratory study measuring the FIBER scale in Canada, Mexico, Saudi Arabia, Spain, and Vietnam. We survey 1464 family owners to enhance SEW construct validity by probing the cross-country measurement invariance of the FIBER scale. Furthermore, we conduct comparative research to investigate how cultural and institutional aspects shape the FIBER dimensions across national contexts. • Socioemotional Wealth FIBER scale is validated in five countries. • FIBER scale’s measurement invariance is assessed for family firms. • Scale items are valid and understood similarly across countries. • Scale items are also compared meaningfully.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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 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".