Bibliographic record
Abstract
Accumulating evidence from diverse fields of inquiry suggests the existence of method hierarchies, where criteria employed by the same epistemic agent constitute a certain preference hierarchy. In this paper, we illustrate the phenomenon of method hierarchy by discussing several prominent studies in clinical epidemiology of coronary artery disease. The current “gold standard” in clinical epidemiology is the randomized controlled trial (RCT) method. Yet, in the absence of studies that satisfy the strict requirement of the RCT method, clinical epidemiologists often relax the requirements of double-blinding, complete follow-up, no treatment switching, and/or randomization. Instead, they sometimes employ less stringent requirements, such as the requirement to account for the potential imbalances between groups through statistical models. This suggests the existence of a certain method hierarchy. However, it is unclear how method hierarchies are to be conceptualized and documented. Specifically, it remains to be seen whether a method hierarchy is best understood as being composed of individual employed methods or as a single composite method with a complex system of if-s and else-s. Suggested Modifications [Sciento-2019-0013]: Accept the existence of method hierarchies. Accept the following definition of method hierarchy: Method Hierarchy ≡ a set of methods is said to constitute a hierarchy iff theories that satisfy the requirements of methods that are higher in the hierarchy are preferred to theories that satisfy the requirements of methods that are lower in the hierarchy. Accept the following question as a legitimate topic of scientonomic inquiry: Conceptualizing Method Hierarchies: should we conceive of a method hierarchy as being composed of individual employed methods/requirements, or should we think of it as constituting one composite method with a system of if-s and else-s, and-s and or-s?
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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 | 0.176 | 0.193 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.006 | 0.049 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".