Bibliographic record
Abstract
This is a note to support and expand recent work on the etymology of German Meer schwein chen, English guinea pig, and related forms with a body of dated evidence, including new first attestations for English guinea pig and Polish świnka morska."Is the English guinea pig a pig from Guinea, and the German Meerschweinchen a piggy from the sea?" Marek Stachowski has asked (Stachowski 2014), returning to the question with a supplemental note on English guinea pig (Stachowski 2018).As he points out, "one cannot but wonder why this small animal, so utterly different from a pig, is nevertheless called a pig, as well as why it should be a pig from Guinea if it does not live in Guinea at all" (Stachowski 2014: 221).Its names in English and German are indeed puzzling.Stachowski's masterly presentation and analysis of the evidence can, I think, be taken even further by a consideration of the dates at which some of the evidence is attested.Let us begin with the second element, pig.Stachowski (2014: 222) notes that "the animal is called a pig also in quite a few other languages (e.g.German Meer schweinchen …)", and discusses the possible relevance of German Meerschwein 'capybara'.The capybara is roughly the size and shape of a small pig, justifying the second element of Meerschwein. 1 The guinea pig, like the capybara, is a furry South 1 Cf.Marcgraf (1648: 230): "figura pene porcorum habet"; Labat (1731: 3.298): "Il différe [sic] peu des cochons terrestres".
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".