Getting the whole story
Bibliographic record
Abstract
Historical research involves the construction of competing narratives around complex historical events. Getting the whole story requires having access to these narratives, which can be a challenge when the coverage of historical research in widely used databases is incomplete or biased. This paper investigates to what extent journals indexed in two historical research databases, namely Historical Abstracts and America: History and Life, are covered by the Web of Science and Scopus, as well as the national and linguistic biases in that coverage. Results show a much higher coverage of historical research in Web of Science than Scopus. However, both databases disproportionately favour indexing English language journals and journals published in the United States and the United Kingdom. That raises questions about how these imbalances in journal coverage may lead to biases in the narratives to which readers are exposed when they limit their sources to those included in large, multidisciplinary databases.
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 | 0.020 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.031 | 0.086 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".