Illusionary Order: Online Databases, Optical Character Recognition, and Canadian History, 1997–2010
Bibliographic record
Abstract
Abstract: It all seems so orderly and comprehensive. Instead of firing up the microfilm reader to navigate the Globe and Mail or the Toronto Star, one needs only to log into online newspaper databases. A keyword search, for a particular event, person, or cultural phenomenon, brings up a list of research findings. Previously impossible research projects can now be attempted. This process has fundamentally reshaped Canadian historical scholarship. We can see this in Canadian history dissertations. In 1998, a year with 67 dissertations, the Toronto Star was cited 74 times. However it was cited 753 times in 2010, a year with 69 dissertations. Similar data appears in the Canadian Historical Review (CHR), a prestigious peer-reviewed journal. Databases are skewing our research. We are witnessing the application of commercial Optical Character Recognition (OCR) technology – originally and primarily designed for the efficient digitization of large reams of corporate and legal documents, conventionally formatted – to historical sources. The results are, unsurprisingly, a mixed bag. In this article, I make two arguments. Firstly, online historical databases have profoundly shaped Canadian historiography. In a shift that is rarely – if ever – made explicit, Canadian historians have profoundly reacted to the availability of online databases. Secondly, historians need to understand how OCR works, in order to bring a level of methodological rigor to their work that use these sources.
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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.030 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".