Bibliographic record
Abstract
Principles of Analogy One clear strategy of argumentation when reasoning about things that are uncertain is to see whether they are similar to things that we do know and then draw conclusions about them on the basis of the similarities. Logicians call this strategy the ‘Argument from Analogy’. An analogy is a comparison of two things or analogues. For example, Julian Huxley offered the following comparison: “The relation between predator and prey in evolution is somewhat like that between methods of attack and defence in the evolution of war.” In comparing these two things, Huxley hopes to shed light on the first pair because of what we know about the second pair. But Huxley is not here providing an Argument from Analogy, and the first thing we should note is not to assume that the presence of an analogy in argumentation means the argument scheme is being used. Huxley provides no details of how the two pairs are alike; nor, crucially, does he draw a conclusion on the basis of the similarities. This latter feature is a key identifying feature of the Argument from Analogy. The basic fallacy associated with the Argument from Analogy is called False Analogy. Understanding how the scheme works does not explain how false analogies can arise and what it is that is wrong with them, but it does help us appreciate how analogical reasoning differs from the other types of reasoning we have discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.049 | 0.009 |
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".