THE LESSONS AND LEGACY OF FRANCES HESSELBEIN—MY PERSONAL REFLECTIONS ON A BELOVED MENTOR AND FRIEND
Bibliographic record
Abstract
Abstract The author is the founder of Women in Leadership and Learning, one of the first leadership development programs for women in the country, and leads her own consulting firm, SHAMBAUGH Leadership. She knew Frances Hesselbein for nearly a quarter century. She notes that “Frances used her strengths generously and selflessly in the service of others, and in doing so, became a driving force shaping timeless leadership principles and management philosophy.” She further recounts indelible impressions of Frances’ quiet power: “She saw in others what was not visible to them and reinforced the power of purpose that is the foundation for what we do and why we thrive. She never failed to leave you with something vital to strive for or do. She always expected you to not only be your best self, but to reach beyond what you thought you were capable of.”
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".