Time to Get Tough: How Cookies, Coffee, and a Crash Led to Success in Business and Life
Bibliographic record
Abstract
Michael J. Coles, the co-founder of the Great American Cookie Company and the former CEO of Caribou Coffee, did not follow a conventional path into business. He does not have an Ivy League pedigree or an MBA from a top-ten business school. He grew up poor, starting work at the age of thirteen. He had many false starts and painful defeats, but Coles has a habit of defying expectations. His life and career have been about turning obstacles into opportunities, tragedies into triumphs, and poverty into philanthropy. In Time to Get Tough, Coles explains how he started a $100-million company with only $8,000, overcame a near-fatal motorcycle accident, ran for the U.S. Congress, and set three transcontinental cycling world records. His story also offers a firsthand perspective on the business, political, and philanthropic climate in the last quarter of the twentieth century and serves as an important case study for anyone interested in overcoming a seemingly insurmountable challenge. Readers will also discover practical leadership lessons and unconventional ways of approaching 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.019 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".