Selling Vero Beach: Settler Myths in the Land of the Aís and Seminole
Bibliographic record
Abstract
Settlers in Vero Beach, Florida, have created a fanciful past for their town, one in which Old Spanish buildings and Indigenous names invoke a past Eden to which white settlers came hoping to make their fortunes. In this story, “noble Indians” slowly vanished sometime between the era of Spanish explorers and the time of Pocahontas. African Americans are barely present, apart from the Highwaymen landscape artists and Zora Neale Hurston. Unclear is who picked the produce. Was it white pioneers themselves? Seminoles? Black Floridians? No matter. The settler story overwhelms such queries with tall tales about fruit made sweeter because of the heat, and about mosquitos bigger than songbirds. Into this mix of advertising rhetoric and heritage tourism, Kristalyn Marie Shefveland steps to point out how stories of Vero Beach repeat, framing it as an Edenic natural paradise, an Old Spanish colony, or an agricultural and tourism gold mine. These stories normalize and naturalize white settler colonialism by romanticizing its past, papering over its violence, erasing Indigenous peoples, and emphasizing the hardships white pioneers overcame. Shefveland is successful at critically illuminating when and where the “firsting” and “lasting” of this narrative shapes popular memory of Indian River and its famous fruit. Shefveland's argument about how this settler colonial past shapes the popular memory of Vero Beach is well made in the book's introduction and conclusion.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".