Bibliographic record
Abstract
I am grateful to Tirthankar Roy for his prompt response, and his generosity in acknowledging the validity of some of my criticisms. It will be obvious to readers that there are some fundamental disagreements between us regarding what constitutes economic history. On this, and a number of other issues that Professor Roy has chosen not to address, it would be tedious to repeat myself; we must agree to disagree and invite readers of this journal to draw for themselves the conclusions they wish—and, ultimately, how fair I have been in assessing his book is something that readers will only be able to judge by reading it themselves. I focus below on the three core issues with which most of his response is concerned: ‘reading the past with reference to the present’, the importance of a ‘region-focused approach’, and the ‘comparative approach’.
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 | 0.009 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.019 | 0.062 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".