Geography and Genealogy: Locating Personal Pasts
Bibliographic record
Abstract
Contents: Locating personal pasts: an introduction, Jeanne Kay Guelke and Dallen J. Timothy Part I Tools, Sources, and Implications for Geography and Family History: The unfolding tale of using maps in genealogical research, Melinda Kashuba Genealogy, historical geography, and GIS: parcel mapping, information synergies, and collaborative opportunities, Mary B. Ruvane and G. Rebecca Dobbs A genealogy of environmental impact assessment, William Hunter Knitting the Transatlantic bond: one woman's letters to America, 1860a 1910, Penny L. Richards Remaking time and space: the internet, digital archives and genealogy, Kevin Meethan. Part II Genealogy as a Cultural Practice: Genealogical mobility: tourism and the search for a personal past, Dallen J. Timothy Genealogy as religious ritual: the doctrine and practice of family history in the Church of Jesus Christ of Latter-Day Saints, Samuel M. Otterstrom Genetics, genealogy and geography, David C. Mountain and Jeanne Kay Guelke Conclusion: personal perspectives, Dallen J. Timothy and Jeanne Kay Guelke Index.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".