Survey of historical databases with longitudinal micro-data
Bibliographic record
Abstract
In 2009, the HSN (now HSNDB) sent out surveys to multiple data centers to document their databases on historical persons. The surveys were part of the 'Towards a Global Life Course' project to improve cooperation between important players in the fields of historical micro-data, which was funded by NWO, IISG, ICPSR and DDB Umea. This database contains the filled out questionnaires, anonymized by obscuring the name, email address, and telephone number of the respondent. At the time they wrote: The historical community is now fortunate to have a growing number of large-scale, public databases of life histories from the past. Some of these databases have been under development for a long time, such as the Demographic Database in Umea, the Utah genealogical database, the Scania database in Lund and the PRDH and BALSAC in Quebec. Others are relatively recent, such as the Historical Sample of the Netherlands, and data collection activities are underway in other European countries as well as Japan and China. For a complete overview of objectives, sources and contents regarding these public databases, see the following questionnaires (available in pdf). - BALSAC population register, Canada - Base TRA Patrimoine, France - COR sample, Belgium - Demographic Data Base, Umeå University, Sweden - Founders & Survivors: Tasmanian life courses in historical context - Geneva Database - Historical Database of the Liège Region - Historical Sample of the Netherlands - Koori Health Research Database 1855-1930, Australia - Melbourne Lying-In Hospital Cohort: 1857-1900 - Norwegian Historical Data Centre - Registre de la population du Québec ancien - Utah Population Database
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 | 0.038 | 0.152 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.033 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.018 |
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".