Place-Conscious Education: Teaching Displacement Using Oral Histories in Virtual Reality
Bibliographic record
Abstract
Abstract Deep in the bayous of Louisiana lies Isle de Jean Charles, home to the tribe of the Jean Charles Choctaw Nation, made up of the tribes of the Biloxi, Chitimacha, and Choctaw, since the early 1800s. At this time the three tribes were distinct and had kept much of their culture intact by isolating themselves from European contact, even if it meant ceding their land. They were seen as “invisible people,” those who kept to themselves to avoid contact with whites. As the remnants of the tribes of the Biloxi, Chitimacha, and Choctaw resettled in Terrebonne Parish, their land encompassed 22,000 acres (about half the size of Washington, DC). After their initial displacements, the tribe suffered loss of land again when oil companies dug more than 10,000 miles of canals straight through the wetlands and brought oil rigs to Isle de Jean Charles. Promised repair of wetlands never happened. Isle de Jean Charles has now been reduced to only 320 acres (which is about five and a half times the size of the National Cathedral in Washington, DC), and, in some places, is only a quarter of a mile wide. Virtual reality lessons have been implemented to teach others by documenting the residents’ voices using their history of place and ancestry and experiences of displacement due to the dramatic loss of land brought on by the destruction of wetlands in the Gulf of Mexico.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".