Overdetermination, underdetermination, and epistemic granularity in the historical sciences
Bibliographic record
Abstract
Abstract The optimism vs. pessimism debate about the historical sciences is often framed in terms of arguments about the relative importance of overdetermination vs. underdetermination of historical claims by available evidence. While the interplay between natural processes that create multiple traces of past events (thereby conducive of overdetermination) and processes that erase past information (whence underdetermination) cannot be ignored, I locate the root of the debate in the epistemic granularity, or intuitively the level of detail, that pervades any historical claim justification network. To reveal the role played by granularity, I elaborate a model of historical claim justification. This model maps out the different elements that enter the justification of historical claims (incl., actual and inferred states of affairs, dating and information reconstructing theories). It also incorporates the different types of processes that affect traces of past events (information creating, preserving, modifying, and destroying processes). Granularity is shown to play a pivotal role in all elements of this model, and thereby in the inferred justification of any historical claim. As a result, while upward or downward shifts in granularity may explain changes about claims being considered as overdetermined or underdetermined, epistemic granularity constitutes an integral part of evidential reasoning in the historical sciences (and possibly elsewhere).
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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 | 0.029 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.057 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".