Bibliographic record
Abstract
GENEALOGISTS have long hesitated to do re-search on Minnesotcis Indian and metis or mixed-blood population. The fact that Indian and related metis peoples participated in a largely ond culture may have convinced diem that few sources were avadable. Even historians, although aware of the existing sources, have shunned a study which appeared to them to have litde value for the writing of genercd history. In spite of such common prejudices, instdutioiis like the Minnesota His-torical Society for a long time have been accumulating resources of real value in genealogical studies of Indians and metis. ^ What wrdten records are available on people who left M I IS'^ccyuTcc'rrons few written records of their own': ' What are the specicd problems involved in doing genealogical research on In-dian and metis famdies'? How can research on individual members of the Indian and metis communities aid in understanding the culture to whicli they belonged'? We hope tliat in examining the pages that follow, readers o/Minnesota History, whatever tiieir ethnic, cul-'The word metis, which means mixed blood in French, was formerly used in Minnesota to refer only to persons of French and Indian mixed tilood in the Red River Valley area. Current usage among Minnesota mixed bloods, however, seems to favor ttie French word, in preference to the English, in referring to alt persons of mixed European and Indian blood. The editors would like to thank Mrs. Rita Schmidt, Wiley Pope, and Karen Petersen for help in preparing this article. Virginia Rogers is a former member of the MHS staff now
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.581 | 0.417 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".