Mining the ‘Internet Graveyard’: Rethinking the Historians’ Toolkit
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
“Mining the Internet Graveyard” argues that the advent of massive quantity of born-digital historical sources necessitates a rethinking of the historians’ toolkit. The contours of a third wave of computational history are outlined, a trend marked by ever-increasing amounts of digitized information (especially web based), falling digital storage costs, a move to the cloud, and a corresponding increase in computational power to process these sources. Following this, the article uses a case study of an early born-digital archive at Library and Archives Canada – Canada’s Digital Collections project (CDC) – to bring some of these problems into view. An array of off-the-shelf data analysis solutions, coupled with code written in Mathematica, helps us bring context and retrieve information from a digital collection on a previously inaccessible scale. The article concludes with an illustration of the various computational tools available, as well as a call for greater digital literacy in history curricula and professional development.
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 | 0.001 | 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