Bibliographic record
Abstract
I a m n o t w h a t y o u t h I n k o f a S h I P -h o P .I am a white, Canadian woman, who at the time of this study had recently moved to the United States to take up an academic position at a private research-intensive college.And neither is Tim. 1 Also white, he is a high school language arts teacher, a haiku poet, runner, and committed bird-watcher who self-admittedly knew nothing about rap music.This disjunction helps explain his students' surprise when Tim announced that the class would be studying hip-hop and spoken word culture in the last term of their senior year.Then he introduced me as the professor who would be coteaching the class.One student asked if he could borrow a tape-recorder, since "there's a lot going on in the school teachers don't know about," a situation he was hoping to change.The "lot" he referred to is the hip-hop poetry, including individual and group (or cipher) improvised freestyling and rapping, which pervades the hallways of urban high schools across the United States but is rarely invited into classrooms.Which doesn't mean that popular culture, and in this case hip-hop, isn't already present in schools, shaping the identities of students and therefore how, what, and why they learn.Tim teaches English, specializing in creative writing, in an urban arts magnet high school in a midsized city in the northeastern United States.In the fall of 2001, I was an assistant professor in a local university's faculty of education.The university suffers from the elitist reputation problems of similar private institutions located in poor urban centers.I had been studying, in theory, the implications of spoken word and hip-hop culture for youth identities and language practices.I met Tim, and he asked me to help him develop and teach a curriculum grounded in hip-hop culture in two of his senior classes.He had been teaching English and creative writing in the city school district for more than twenty years and felt that his ignorance about v i i i p r e f a C e
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.627 | 0.449 |
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".