Bibliographic record
Abstract
Purpose This paper aims to explore some of the strategies and issues associated with writing historical research to meet the demand for social “relevance” and to appeal, and be accessible to, a broader audience of readers. Design/methodology/approach The paper uses Brown and Hackley as a foil for identifying the key differences between traditional academic writing and writing to get noticed. These differences are then analyzed to identify the issues for academic historians. Findings The paper highlights distinct uses of rhetoric, metaphor and theory in Brown and Hackley that make their paper stand out from typical academic history papers and raises concerns about this style of research and writing. Originality/value The paper identifies and opens the debate on some key issues in historical writing and explanation that arise when academic historians take seriously the demand to seek greater contemporary relevance and public support for their research.
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 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.035 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".