Book Review - Dare to Lead: Brave Work. Tough Conversations. Whole Hearts, Brené Brown
Bibliographic record
Abstract
Dare to Lead: Brave Work. Tough Conversations. Whole Hearts is a much loved, seminal leadership text within the field of social work. On more than one occasion I have received copies of this book as a gift from students and colleagues who feel very passionately about Brené Brown’s approach to leadership, and who want to ignite this shared passion within me. Brown describes herself as a “researcher, storyteller, and … Texan who’s spent the past two decades studying courage, vulnerability, shame, and empathy” (brenebrown.com). Brené Brown’s eight books, countless keynote addresses, regular international speaking engagements, and TV talk show pops-ups make her the most notable social worker of all time, save for (perhaps) Jane Addams? Brown’s approach can be found via multiple media, podcasts seek to summarize her work in fifteen minutes a day, while her YouTube hosted TED-talks distil her wisdom about people, their emotional lives, and, to a lesser extent, the role of social work in leadership through pithy, PowerPoint-supported lectures.
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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.078 | 0.072 |
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".