Ain’t No Mountain High Enough Review: How Children Succeed: Grit, Curiosity, and the Hidden Power
Bibliographic record
Abstract
There is no obstacle in the path of young people who are poor or members of minority groups that hard work and preparation cannot cure. ~ Barbara Jordan, a leader of the Civil Rights movement Paul Tough’s work reflects his enduring concern about the achievement gap in our society between children from the poorest and wealthiest families in the United States. In his first book, Whatever It Takes: Geoffrey Canada’s Quest to Change Harlem and America, he chronicled the creation of the Harlem Children’s Zone, Inc., an organization that has received national acclaim for its efforts to pull children out of the intergenerational cycle of poverty. Tough seeks to dispel the notion that children from underserved communities are doomed to fail. The basic premise is that, regardless of a child’s IQ, she or he can excel through hard work and perseverance—if given proper encouragement and opportunity. Rather than dwelling on sobering national statistics, which do little to move even the most well-intentioned reader to action, he focuses on the human element. He does this by showing what is possible through the success stories of individual children, teachers, and
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".