Case study: Fit 2 Excel? Family. Business. And family business.
Bibliographic record
Abstract
Fit 2 Excel traces the experiences of Jon and Sheila Stawinski, co-owners of two fitness related businesses (Fit2Excel and Injury2Excellence in Chittenden County, Vermont. As their commercial activities experience rapid growth, the entrepreneurs find themselves immersed in many personal challenges that appear to be inherent in family-run enterprises, and soon find themselves stressed, burdened, and distracted from family matters. As their lives become increasingly frenzied Jon and Sheila must wrestle with pertinent questions regarding business, family, and the balance between the two. The Instructor’s Manual to the case includes analysis that draws upon the Segmentation-Integration Continuum of Family Business and the model of Differential Permeability of Identity in Family Business (Sundaramurthy & Kreiner, 2008), as well as consideration of issues of strategic planning in family business contexts (Jaffe, 2005). The case is intended for use in undergraduate entrepreneurship and family business courses, and \nspecific learning objectives include: (a) managing business/family overlap/integration, (b) establishing boundaries in family business, and (c) the strategic management of family business.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.012 |
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".