Falsificationism redux: in search of explanatory rationality in historical sociology
Bibliographic record
Abstract
Those who study unique events and processes cannot manipulate the world to ‘test’ theories, to ensure conclusions are rational, as falsificationism prescribes. This has left historical sociologists and kindred researchers to use hermeneutics, forms of counterfactual reasoning, and covering laws, but these techniques do not ensure explanations are accountable to the object of inquiry. I repurpose the falsificationist principle of negativity to serve rational theoretical redescriptions of this class of objects. We must work in a theoretical medium, as the main criticism of falsificationism maintains. Theoretical prejudices can nevertheless be held in check, making space for new conclusions, via the double break characteristic of scientific reflexivity. A directional dynamic toward rational explanations can then result by starting with negative cases, which inherently bring case-theory misfits into focus, and applying immanent critique to cycle from worse to better such fits. This allows us to manipulate our theories to ‘test’ the world.
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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 | 0.040 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.087 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".