Bibliographic record
Abstract
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.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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; a candidate call from one teacher head, 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".