Reichenbach Falls—And Rises? Reconstructing the Discovery/Justification Distinction
Bibliographic record
Abstract
The distinction between ‘context of discovery’ and ‘context of justification’ in philosophy of science appears simple at first but contains interesting complexities. Paul Hoyningen-Huene has catalogued some of these complexities and suggested that the core usefulness of the ‘context distinction’ is in distinguishing between descriptive and normative perspectives. Here, I expand on Hoyningen-Huene’s project by tracing the label ‘context of discovery and context of justification’ to its origin. I argue that, contrary to initial appearances, Hans Reichenbach’s initial context distinction from 1938 does not easily map onto Hoyningen-Huene’s distinction between descriptive and normative perspectives on science. However, this is not a reason to reject Hoyningen-Huene’s simplified context distinction, nor do I recommend returning to Reichenbach’s initial proposal. It is, however, further reason to believe that the context distinction does not have a single, easily understood meaning. Along the way, I revisit Reichenbach’s version of ‘rational reconstruction’ and highlight its usefulness as a tool for philosophy in general.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".