Bibliographic record
Abstract
One of the greatest historical traumas experienced in the United States was our Civil War.Over 600,000 Americans died in that war, and chattel enslavement-described in Mississippi's 1861 secession declaration as "the greatest material interest of the world" (Civil War Trust, 2017)-was abolished.The rebelling southern states were devastated.African Americans, long depicted as form of degraded farm animal, had proved themselves equal in humanity and battle and were voted into power across the South.In the 150 years since the war ended, Americans have managed the war's trauma by constructing accounts that organize the carnage around great moral truths (Blight, 2001;Cobb, 2005).Equality was one of those moral truths, but it was eclipsed by a spirit of reconciliation that bound North and South together around a shared ideology of White supremacy.White supremacy became the "moral" and "scientific" truth that justified the restoration of racialized power and ended the brief experiment in social and political equality.The triumph of that restoration was commemorated by commissioning monuments to the Confederacy and placing them in prominent spaces in cities and towns (Leib, 2002).Cast in bronze and placed upon granite pedestals, they were built to last.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.031 | 0.116 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 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".