Bibliographic record
Abstract
In August 2014 I lie for hours, motionless and bloodied on the ground in Center City Philadelphia before Robert Indiana’s iconic LOVE statue, in silent protest of police brutality. It went viral. Overnight I became ‘the guy who always talks about race.’ Soon people wanted my opinion on every aspect of critical race theory imaginable. I had to educate myself, and quick, because what they didn’t understand is that my burning desire to react to this pressing issue didn’t come from readings in books. It came from a deep fear and disappointment in my lived experience as a Black person in this country, and I could no longer be silent. It was a long time before I found the beauty and value in my lived, Black experience. In October 2015 I was able to combine that experience with my artistic skill as an actor; writing and performing, THE BITTER GAME. This piece of theater effectively combined all that petrifies me as an actor and human; devised theater, solo performance, semi-autobiographical storytelling and yes… basketball. The sleepless nights and mounting anxiety I daily felt while sitting to put pen to paper, attempting in my own way to address such a polarizing topic as excessive police force, was paralyzing. My heart break daily for the families of the victims whom I used as source material. However, through it all, I was reminded of these two truths: for the artist, heartbreak is a rite of passage and that the thin line between art and activism must be traversed with courage.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.089 | 0.057 |
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".