A decision architecture for epistemic prioritization: Machine learning at the intersection of technology and society
Bibliographic record
Abstract
This review examines how machine learning (ML) methodologies are transforming the philosophy of science and engineering through five critical epistemic functions: Prediction, Explanation, Discovery, Understanding, and Decision-making (P.E.D.U.D.). We analyze each function individually and then provide examples of how ML applications embody these epistemic aims. Building on this analysis, we develop a framework to help users/practitioners determine which epistemic function to prioritize for specific problem domains by creating a decision architecture that aligns ML methodologies with epistemic goals. Finally, we explore the broader philosophical implications of this epistemological landscape by analyzing tensions between data-driven and theory-driven approaches and argue that ML necessitates a reconsideration of the traditional philosophy of science as the balance between these five functions evolves. • ML reshapes science via Prediction, Explanation, Discovery, Understanding & Decisions. • Develops a decision framework aligning ML methodologies with targeted epistemic goals. • Highlights tensions between data- and theory-driven science approaches in ML fueling!. • Examines ML's epistemic shift urging a reappraisal of science's classic frameworks … • Outlines challenges and future research directions at ML's epistemic nexus for change!!.
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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 | 0.027 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".