Bibliographic record
Abstract
W hat is it about hip-hop culture that has allowed it to defy its critics and pronouncements by media pundits that it would only be a passing youth trend ? What has allowed all its artistic elements to proliferate globally and take root across the world in greatly disparate societies? Countries both in proximity to and far away from American borders, as well as those localities continually in the throes of political warfare, often reflect today’s hip-hop culture and style that is being exported by Viacom’s MTV and BET, the major recording distributors, and any number of multinational clothing lines in the United States. For instance, Toronto, Canada, just across the U.S. Canadian border, is in proximity to New York City and benefits from having immediate access to some of the seminal U.S. emcees and b-boys. Toronto has, therefore, enjoyed a long-term close relationship with hip-hop, and has created local Canadian deejays such as Ron Nelson, who produced many successful early concerts with Run DMC, Public Enemy, KRS-One, and Big Daddy Kane. The predictable outcome of this geographical proximity is that it spawned early Canadian emcees, such as Ken E. Krush and the Dream Warriors, as well as several breakers and graffiti artists, particularly in the Toronto suburbs of Scarborough and Mississaugua. 1 In contrast, global sites remote from U.S. hip- hop urban centers, such as the Palestinian West Bank, have less direct contact and, therefore, a more generalized influence.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.111 | 0.018 |
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".