Using a household sampling frame to study family businesses: The 1997 national family business survey
Bibliographic record
Abstract
The Family Business Research Group (FBRG) attempts toexplore the relationships between family and business activities within familyfirms by using a household sampling frame (rather than the traditional businesssampling frame). The FBRG is a group of researchers located at 16 U.S. land grantuniversities and the University of Manitoba. The result of the FBRG's effortswas the 1997 National Family Business Survey (NFBS), which required 794households to complete one business and one household interview. The NFBS was designed to analyze the structure, communication patterns,decision-making processes and management strategies in family owned businesses,and included questions on marketing strategies, the customer base and localeconomic viability.This research builds on work previously completed for'At-Home Income Generation: Impact oon Management, Productivity, and Stabilityin Rural-Urban Families,' also called the 'Nine State Study.' The FBRG survey has spawned numerous analyses and studies--e.g., a 1999study that uses the 1997 NFBS data indicates that the average small communitybusiness respondent was 46 years old, white, male, and married.The 1997NFBS challenges conventional notions of family-owned businesses as static andlinear. (SAA)
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".