Bibliographic record
Abstract
As an athlete whose actions receive enormous amounts of media attention, Colin Kaepernick chose to use his powerful position to bring worldwide attention to the unjust murders of innocent black men at the hands of American police officers by kneeling silently during the national anthem before football games.Kaepernick was soon joined by other players in the NFL who shared his view that the anthem and the flag, both symbols of the nation, do not represent Americans as they should (Miller, 2017, para.3), since the police officers who killed Alton Sterling and Philando Castile-to name a couple-were given paid leave instead of being found guilty of murder.The movement, "taking a knee", is an example of how nonverbal communication can have indexical meanings founded in cultural ideologies.Following Silverstein (2003), this paper traces the semiotic trajectory of kneeling, from its traditional, first-order index of respect and humility, to its second-order index of protesting police brutality, its third-order index of disrespect for the nation, and, finally its fourth-order index of solidarity and retaliation to insult, ending with a discussion of the role of patriotism in the ordered indexes.The aim is to show that even gestures can have indexical ordering.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".