Shooting the Archives: Document Digitization for Historical–Geographical Collaboration <sup>1</sup>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This article focuses on the practical and methodological dimensions of a somewhat‐neglected aspect of so‐called ‘digital history’: user digitization of historical documents for research projects. Increasing numbers of professional researchers, including historians and historical geographers, are embracing digital technologies as a way to speed research, collect large amounts of primary source material, and enhance their use of this material by mobilizing it from its institutional context. Yet few scholars or information managers have reflected on the implications of this vast, decentralized and idiosyncratic digitization exercise. Debates over digital history have focused mainly on the role and place of archives in the digitization of historical sources or the collection and preservation of digitally created sources, or the merits of the application of new information technologies to historical research. In this short reflection on our own research process, we consider the trend towards self‐digitization of archival sources, and share our practical experiences of document digitization for research and collaborative purposes. We contend that practitioner document digitization opens up exciting new methods for reading and analysing documents, in particular possibilities for enhanced scholarly collaboration.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it