Bibliographic record
Abstract
This chapter deals with the second approach to doing philosophy historically, which I have called the diagnostic approach. This approach is rooted in the fact that philosophical pictures can be deceptive. A picture may be widely accepted: it may serve as the unquestioned starting point for a great deal of our thinking, and we may take for granted that we understand it. But it may have a hidden significance that escapes us. It may have far-reaching effects on our thinking, perhaps negative ones, that we fail to notice. When this happens, we frequently find it necessary to diagnose the picture. We inspect it with a suspicious eye, in the hopes of discovering its true nature and unearthing the ways in which it distorts our thinking. Typically, this involves tracing the picture's origin: examining how it came into existence, how it came to govern our thinking, and what it led us to neglect in the course of doing so. In returning to the picture's origin, we learn how and why it began to deceive us. We may also discover alternatives to it, competing pictures that it supplanted and that have long been overlooked. Diagnosis of this sort often serves as a form of therapy. Pictures deceive us when we fail to understand their true nature or recognize their effects. In other words, pictures deceive us when we fail to reflect on them. Reflecting on how a picture came to deceive us helps to lessen its hold on us.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".