Bibliographic record
Abstract
The historical memory of the settlement of the Oregon Territory was crafted in a genre of memoirs published in magazines and newspapers in the decades around the turn of the 20th century. These narratives minimized the complexity of the events, smoothed over the contradictions and genocidal violence of settler colonialism, and erased the diversity of the participants. Microhistory deconstructs the singularity of large historical mythopoetics by contextualizing small scale events, interactions, and relationships, and is particularly appropriate to historical archaeologies that engage the primary documentation and materiality of war and conflict, where the events tracked by the archaeological record are particularly fine grained. I present three stories focused on the Rogue River war of 1855–56, an indigenous rebellion against American colonialism: how two Black settlers were killed defending their fellow settlers from Indigenous combatants, how a Métis person from eastern Canada helped lead the rebellion despite being thousands of miles from his home, and how a young military officer from Charleston, South Carolina, argued for the rights of indigenous people, despite being the son of the Governor of South Carolina and the owner of over 600 African slaves.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".