What the Rationalism-Empiricism Debate Is Really About
Bibliographic record
Abstract
Abstract While Chapter 1 provided a sketch of the basic outlines of the rationalism-empiricism debate, this chapter presents a fully developed account of how we understand the debate, comprehensively rethinking the theoretical foundations of the debate. Many commonly held views of what the debate is about are deeply flawed, while others, which may be closer to ours, aren’t spelled out in enough detail to meaningfully address the concerns that critics of the debate have raised or to do justice to the many factors that organize the space of options within this debate. This chapter develops an account of the debate that overcomes these limitations, introduces key terminology that we rely on in later chapters, and clarifies a number of key theoretical notions that are at play in the debate (such as domain specificity and domain generality).
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".