Bibliographic record
Abstract
This poem is dedicated to Geoffrey Canada and the Harlem Children’s Zone and to Luis Rodriguez. I wrote this poem in response to the frustration I was feeling while delving into policy questions and strategies with various community leaders and think tank wonks about how to reverse the disparities of young men and boys of color in California. I am an original member of the Alliance for Boys and Men of Color, a coalition of community based organizations, researchers, foundations, and public systems engaging in a 10-year strategy to improve outcomes for boys and men of color, which was started by The California Endowment. I noticed and felt a number of things during the first two years of this work. There was a lot of deficit-focused talk and a lot of attention paid to the overwhelming negative elements affecting boys of color (African-American = Black, and Latino = Brown), including health disparities like disease and violent death, a 50% high school dropout rate, a lack of knowledge of cultural identity and history, etc. Though we were talking about breaking out of system silos (i.e. ways of evaluating and treating these youth from institutionalized perspectives – hospitals, schools, prisons, social services – without sharing information), I felt like we were not focusing on what Black and Brown communities have in common. It seemed to me that once we had identified our shared problems, we needed to identify our shared assets. However, many of the solutions were not focused on healing, or common strengths, or even culture as a solution. The focus was often on money and laws. I felt that I needed to write a poem that also addressed the demographic shift towards a brown America, where the questions of race and how these questions were applied to policy and political discourse should no longer be dominated by a Black-White dichotomy. Our reality forces us to move toward a discussion of a brown America. Brown mixes all the colors. We need to use our shared history as an asset, a means to heal, and a trusted guide into the future.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 teacher head, 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".