The Emotional Labor of “Taking a Knee”
Bibliographic record
Abstract
We center three publicly accessible images: (1) Am I not a Man and a Brother? (1787), (2) Colin Kaepernick (2017) “Taking a Knee”, (3) Mother McDowell of the Black Student in Florida Admonished for “Taking a Knee” in school (2017). The photograph of mother McDowell is included, rather than her son, who she wanted to remain anonymous across media outlets. We draw primarily from publicly accessible media and scholarship available via the Internet (museums, newscasts, scholarly repositories) to provide a composite of kneeling discourse and counter-narratives related to race (i.e., anti-slavery, abolition, anti-racism protests) and proper behavior. Each image is situated within literature supporting analysis through concepts (time, race) visual, and textual information. Rather than detailing the images, we focus on the surrounding narratives, contemporary readings, redactions, and annotations (we create or relate to) to consider emotions as part of the context, impetus, and force behind the actions captured in them. We juxtapose, redact, and critique images and texts associated with kneeling/taking a knee by men and boys racialized as Black, but not exclusively., as the practices we illustrate in response to structural racism (i.e., discipline in schools) also bring attention to events involving other students: a Black girl and an Indigenous (Inuit) boy.
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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 | 0.003 |
| 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 | 0.020 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".