Bibliographic record
Abstract
On November 20, 1943 young Higgins landing craft operator Leon Cooper ferried U.S. Marines of the Second Division ashore on the island of Betio, Tarawa Atoll. The Tarawa landing was to be the first American amphibious assault in World War II and at Red Beach it challenged an entrenched force of 5,000 Japanese soldiers. The fighting lasted only seventy-six hours but it remains one of the bloodiest offenses during WWII. Nearly 1,700 Americans died and over 2,000 wounded during the battle at Tarawa. Most of the dead, both American and Japanese alike, were quickly buried in unmarked graves and cemeteries on the island. Since WWII, some American remains have been recovered, accounted for, and returned to the United States. However, the remains of 564 U.S. Marines and countless Japanese soldiers have yet to come home. Several years later... Leon Cooper had returned home and made a life as a computer company executive. Since retiring, the 89 year old Malibu veteran has stumbled upon a new fight in a familiar place. A few years ago he learned of a landfill covering the bodies of his fallen comrades at Red Beach. Outraged he began spending his own money and raising awareness about the build-up of garbage and debris on hallowed ground. His efforts have resulted in nationwide recognition and a documentary, Return to Tarawa: The Leon Cooper Story. However, the ultimate goal has yet to be realized. Leon has made it his last work to clean-up Red Beach. Apart from local and regional governments not cooperating, rising sea levels have also posed a threat to Leon's efforts. With an intending doom dictating the timeline, the clean-up of Red Beach pales in comparison to the relocation of 10,000 natives from their homes in one of the poorest regions on earth. If not addressed the issues at Red Beach will simply be underwater. The moral line made at this juncture divides right from wrong and outlines actions in each. Are mounds of trash atop soldier's remains simply 'out of sight and out of mind'? Or is their trash in our oceans atop forgotten hero's buried at sea? History tells a story of man's inhumanity to man; a tale of war that led men to kill other men for shared principals and the translation of that naivety into a nation dumping trash on a graveyard. It is the goal of this thesis to explore the qualities of humankind that are embodied in a soldiers sacrifice by creating an architecture that controls degrading conditions in spite of certain destruction. This, a municipal waste management facility, chooses to hold the sacrifices of soldiers above their deaths and carries their bodies from the trash into a memoriam that eases trauma into memory while instituting new possibilities, new activities, and new images for a hopeful future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".