Bibliographic record
Abstract
There is a rising awareness of the tools of Geospatial Information Systems on the part of both amateur and professional historians. Professionals (historians, political and social scientists, and even medical historians) are able to see and think about various trends in a more visual and useful way to them (think of seeing how various diseases spread and where and why), while amateurs seeking more information of their ancestors can also benefit by seeing migration patterns and places of origin, which could help them think about why their ancestors left a place to immigrate to a new country.Knowing who controlled what land and when can make the task of finding appropriate records, for any purpose, a bit easier. Also mentioned are grass roots initiatives, that is, not created by governments or commercial organizations, but by local genealogical and historical groups. This brief overview, done primarily from a layman's viewpoint, can engage the reader with an idea of how to get their work more appreciated and out "into the world". A study by a student at California State University at Fullerton mentions that such genealogy researchers tend to be generative (that is, concerned with passing information along to those following), and very aware of themselves and their ancestors in a time and place.Hopefully this will get more interaction between academics and people out in the world who can appreciate their work.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.015 |
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".