New business histories! Plurality in business history research methods
Bibliographic record
Abstract
We agree with de Jong et al.'s argument that business historians should make their methods more explicit and welcome a more general debate about the most appropriate methods for business historical research. But rather than advocating one ‘new business history’, we argue that contemporary debates about methodology in business history need greater appreciation for the diversity of approaches that have developed in the last decade. And while the hypothesis-testing framework prevalent in the mainstream social sciences favoured by de Jong et al. should have its place among these methodologies, we identify a number of additional streams of research that can legitimately claim to have contributed novel methodological insights by broadening the range of interpretative and qualitative approaches to business history. Thus, we reject privileging a single method, whatever it may be, and argue instead in favour of recognising the plurality of methods being developed and used by business historians – both within their own field and as a basis for interactions with others.
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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 | 0.341 | 0.287 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.007 | 0.044 |
| Scholarly communication | 0.022 | 0.029 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".