Bibliographic record
Abstract
This paper provides a principled and elegant solution to the Gettier problem. The key move is to draw a general metaphysical distinction and conscript it for epistemological purposes. Section 1 introduces the Gettier problem. Sections 2– 5 discuss instructively wrong or incomplete previous proposals. Section 6 presents my solution and explains its virtues. Section 7 answers the most common objection. 1. The Gettier Problem Lore has it that before 1963 many philosophers thought knowledge was justified true belief, which view met its doom in Edmund Gettier’s 1963 paper “Is Justified True Belief Knowledge?”. Gettier produced two cases wherein intuitively the subject gains a justified true belief but fails thereby to know, demonstrating that knowledge differs from justified true belief, the latter not sufficing for the former. Examples in this mold we call Gettier cases. Gettier cases follow a recipe. Start with a belief sufficiently justi-1 Manifest Failure 2 fied (or warranted) to meet the justification requirement for knowledge. Then add an element of bad luck that would normally prevent the justified belief from being true. Lastly add a dose of good luck that “cancels out the bad, ” so the belief ends up true anyhow. It has proven difficult to explain why this “double luck ” prevents knowledge. 1 Here are two Gettier cases to focus our discussion. (FORD) Sarah observes her trusted colleague, Mr. Nogot, arrive at work driving a new Ford. Nogot reports to Sarah that he is ecstatic with his new Ford. Sarah has no reason to mistrust him, so she believes Nogot owns a Ford. From this she infers that someone in her office owns a Ford. But Nogot uncharacteristically is playing a practical joke on Sarah: he doesn’t really own a Ford. Nevertheless, unbeknownst to Sarah, Mr. Havit, the newly hired clerk on his first day in the office, does own a Ford. 2 1 My characterization is modeled on Zagzebski’s 1994: 66; 1996: 288–9;
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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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.006 | 0.029 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.032 | 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".