Running for their lives : girls, cultural identity, and stories of survival
Bibliographic record
Abstract
Chapter 1 Chapter Acknowledgments Chapter 2 Chapter Introduction: Girl Problems Sherrie A. Inness Chapter 3 Acknowledgments Chapter 4 Introduction: Girl Problems Part 5 I: Dancing the Bashenga: Girls Confront Racism Chapter 6 1 Coming to America: Not the Movie by Eddie Murphy Chapter 7 2 From Foutou to French Fries: Life on the Edge of African and American Cultures Chapter 8 3 Mommy I just want to fit in!: An African Girl's Story Chapter 9 4 Childhood Misconceptions: Reflections of a Biracial American (Colored) Girl Chapter 10 5 Mah-Rukh Ali: Profile of a Norwegian Chapter 11 6 Fighting Shame: A Somali Single Teen Mother in Canada Part 12 II: Don't Look Down: Girls Living on the Edge Chapter 13 7 A Tightrope Made of Sari Silk: The Delicate, Perilous World of Girlhood in India Chapter 14 8 Afia's Story: A Guide to Survival Chapter 15 9 A Tryst Missed: Girlhood in Pakistan Chapter 16 10 Horizontal Rain: Road from Sarajevo Chapter 17 11 Charley Lauren's Story: Growing-up in the Shadow of Mental Illness Chapter 18 About the Contributors
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".