Bibliographic record
Abstract
Song collecting took wing in eighteenth-century Europe under three impulses: political, colonial, and economic. ‘Through music our race was humanised; through music it will attain greater humanity,’ wrote the Prussian philosopher Johann Gottfried Herder (1744–1803). When he coined the term Volkslied – ‘folk song’ – it was to exemplify the soul of a people; folk song would be the quintessential expression of Romantic nationalism, and its character would reflect the character of a nation. The concept didn’t operate in terms of rich or poor, educated versus peasantry; it embraced everyone. Herder argued that it was a patriotic duty to collect Volkslieder as devotedly as he himself did, before they disappeared. The second impulse behind the new vogue was colonial curiosity: what did the music of the subjugated peoples – and the hoped-for Christian converts – actually sound like? The Swiss theologian Jean de Léry (1534–1611) wrote about the music of Brazil, using musical notation and describing antiphonal singing between men and women; Captain James Cook (1728–1779) described the music and dance of Pacific islanders. Jean-Jacques Rousseau’s Dictionnaire de musique (1768) was the first book to deal with the ‘diverse musical accents’ of other lands, giving samples of Swiss, Iranian, Chinese, and Canadian Amerindian music. The most graphic colonial account came from the Scottish explorer Mungo Park (1771–1806) on his journey along the Niger River. Repeatedly attacked by tribesmen, and several times imprisoned by their suspicious rulers, he was at one point taken in, fed, and lodged with great generosity by the women of a Malian village, who allowed him to watch as they spun cotton and sang through the night. ‘They lightened their labour by songs,’ he wrote, one of which was composed extempore; for I myself was the subject of it. It was sung by one of the young women, the rest joining in a sort of chorus. The air was sweet and plaintive, and the words, literally translated, were these: ‘The winds roared, and the rains fell. The poor white man, faint and weary, came and sat under our tree. He has no mother to bring him milk; no wife to grind his corn’ – Chorus: ‘Let us pity the white man, no mother has he’ etc etc.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.038 | 0.013 |
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".