Bibliographic record
Abstract
In the space where photographs elicit memory, I wade between the contours of the metaphor and our ever-present reality to look ahead. The photograph below portrays my cousin Bryan and our favorite pastime growing up. As kids, we would often challenge each other to one-on-one games of basketball. I can still remember my insistent hope that he’d miss every shot. We’d yell “brick!” at one another well before the ball left our dirty fingertips. Every miss became a celebratory moment, and each misstep an inch closer to trading possessions. Now more than ever, I hope he makes every shot. In basketball, as in life, we aim for something. From the moment we bend our knees and push away from the ground that holds us, there’s no promise of whether the ball goes in; nor is there a guarantee that we will get to play the game again. The precariousness of Black life makes me wary of the possibility of a short game. I hope that we get to play all four quarters, on our terms; I hope that your ball is full of air and your shoes tied tight; I hope your arch is pure and that when the ball goes in the rim, it makes the sound of a hoop with no net.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.066 | 0.015 |
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".