Bibliographic record
Abstract
The field of education has been and will continue to be essential to the survival and sustainability of the Black community. Unfortunately, over the past five decades, two major trends have become clearly evident in the Black community: (a) the decline of the academic achievement levels of Black students and (b) the disappearance of Black teachers, particularly Black males. Today, of the 3.5 million teachers in America’s classrooms (AACTE, 2010) only 8% are Black teachers, and approximately 2% of these teachers are Black males (NCES, 2010). Over the past few decades, the Black teaching force in the U.S. has dropped significantly (Lewis, 2006; Lewis, Bonner, Byrd, & James, 2008; Milner & Howard, 2004), and this educational crisis shows no signs of ending in the near future. As the population of Black students in K-12 schools in the U. S. continue to rise—currently over 16% of students in America’s schools are Black (NCES, 2010)—there is an urgent need to increase the presence of Black educators.The overall purpose of this edited volume is to stimulate thought and discussion among diverse audiences (e.g., policymakers, practitioners, and educational researchers) who are concerned about the performance of Black students in our nation’s schools, and to provide evidence-based strategies to expand our nation’s pool of Black teachers. To this end, it is our hope that this book will contribute to the teacher education literature and will inform the teacher education policy and practice debate.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.044 | 0.011 |
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".