Rethinking<i>Turner v. Keefe</i>: The Parallel Mobilization of African-American and White Teachers in Tampa, Florida, 1936–1946
Bibliographic record
Abstract
In 1941, members of the local unit of the Florida State Teachers Association (FSTA) met in Tampa to plan a lawsuit against Hillsborough County's school board for paying African-American teachers less than white teachers. Hilda Turner, who taught history and economics at Tampa's historically black high school, agreed to serve as plaintiff; she was the only one to volunteer. Thurgood Marshall chief counsel for the National Association for the Advancement of Colored People (NAACP)'s Legal Defense Fund (LDF), assisted Samuel McGill, a Jacksonville attorney, in representing Turner, who filed a complaint in federal court that November. In the fall of 1942, responding to Turner's suit, Hillsborough County school board dropped the race-tiered salary schedule and adopted a “rating” scale that based teachers' pay on a number of factors other than training and experience, including “physical, health, personality, and character,” “scholarship and attitude,” and “instructional skill and performance.” The rating committee charged with classifying teachers placed 84 percent of white teachers in the highest pay bracket, and 80 percent of African-American teachers in the lowest pay bracket. As in other Florida cases, Hillsborough County school board offered the new rating scale as evidence that the district no longer discriminated on the basis of race, an assertion Marshall attempted to challenge at trial. However, in 1943, two years after Turner's complaint was originally filed, the federal district judge ruled that the new salary scale was “fair on its face.”
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.017 | 0.017 |
| 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 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".