Digital History Anthologies on the Web:<i>German History in Documents and Images</i>
Bibliographic record
Abstract
Initial public offerings (IPOs) in the dot-com world do not always turn out to be the darlings they are expected to be. Ask Mark Zuckerberg about Facebook's IPO in May 2012. But even successful new ventures often defy their founders' expectations. As we hope to suggest in the following report,German History in Documents and Images(GHDI)—a project that has put thousands of primary source texts, drawings, photographs, and maps on the internet, along with hundreds of pages of accompanying commentary—has drawn critical appreciation from specialists and nonspecialists alike, but it has also raised thorny questions about authorship, authority, and audience. Those questions concern the writing of history in general and the newer, more specific discipline of “history on the web.” Like the project itself, this report is the result of a collaboration among the GHDI project staff, which is based at the German Historical Institute (GHI), Washington, D.C., and the GHDI volume editors, all of whom teach (or taught) German history at colleges and universities in North America. In the following pages, we will discuss the origins and early goals of the project, describe the challenges associated with the realization of a large, collaborative history project of this nature—whether in book or digital form—and reflect upon what we perceive as the promise and perils of digital history anthologies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".