An analysis of the Arts and Humanities submitted research outputs to the REF2014 with a focus on academic books
Bibliographic record
Abstract
This report and dataset analyses the research outputs, and monographs in particular, from the Research Excellence Framework 2014 (REF2014) as part of the AHRC-funded research project titled: The Academic Book of the Future (https://academicbookfuture.org/). The REF2014 submission information delivered to HEFCE provides a rich data set that can provide a means of finding out more about the academic books submitted in the last REF cycle (2008-2013). The analysis of the data provides useful indicator data about academic book writing and publishing, and will further augment the analysis already provided by HEFCE. The research focuses upon the Main Panel D for Arts and Humanities. Within this Panel, data can be investigated by Unit of Assessment Subject Area and by Research Output Type. The HEFCE data was mined for ISBN data which was compared against bibliographic catalogues held at The British Library to provide additional supporting data.
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 | 0.016 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.041 | 0.082 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.018 |
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".