Bibliographic record
Abstract
The incredible, little-known story of the first successful Black woman in the sport of auto racing in the United States. Early in her career, Cheryl Glass looked like a lock to become the first Black woman to compete in the Indianapolis 500. From racing quarter midget cars at ten years old to Indy Lights in her twenties, Cheryl was on her way towards a winning career in auto racing. InThe First Lady of Dirt: The Triumphs and Tragedy of Racing Pioneer Cheryl Glass, Bill Poehler tells Cheryl’s full story for the first time. He recounts how Cheryl rapidly became the first successful Black woman in the sport, yet frequently encountered racist and sexist taunts from other drivers and fans throughout her career. While appearing to have it all—talent, ambition, looks—she faced many challenges on and off the track and her life soon spun out of control. Featuring exclusive interviews with Cheryl’s mother, friends, and competitors,The First Lady of Dirttakes you behind the scenes and in the driver’s seat of Cheryl’s life. Poehler, an amateur racer himself, places the reader at the track, smelling the dirt and fumes, hearing the roaring engines and crashing metal, and feeling Cheryl’s joy and pain. It’s the inspiring story of a racing pioneer and a tragic tale of the pressures that are often hidden from public view until it’s too late.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.015 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.091 | 0.030 |
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".