Shooting the Archives: Document Digitization for Historical–Geographical Collaboration <sup>1</sup>
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and methodological dimensions of a somewhat‐neglected aspect of so‐called ‘digital history’: user digitization of historical documents for research projects.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.
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 | 0.021 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".