Bibliographic record
Abstract
Etymologically, nostalgia is a longing to return home. Music, as a temporal art, has numerous ways of suggesting or magnifying a vanished past, whether fantasized or actually experienced. This issue of Volume! offers a whole scope of various insights on the phenomenon of nostalgia, within a diversity of genres: French chanson, Canadian country song, cold wave… In turns a generational phenomenon, a marketing tool or an aesthetic cement for diverse musical communities, the protean aspect of nostalgia is investigated by the international contributors to this issue from a broad spectrum of social sciences. This issue aims at taking part in the pluridisciplinary debate on the central importance of nostalgia within popular music. La nostalgie est le mal du retour. La musique, art du temps, a de nombreuses manières de faire revenir, de suggérer, ou de magnifier un passé disparu, connu ou fantasmé. Ce numéro de Volume! offre un florilège d’analyse du phénomène nostalgique sur des terrains aussi variés que le rock rétro d’aujourd’hui, la chanson française, la chanson country canadienne ou la cold wave... Tour à tour phénomène générationnel, ruse marketing ou ciment esthétique de communautés diverses, l’aspect protéiforme de la nostalgie sera saisi par les différents contributeurs internationaux de ce numéro selon différentes approches des sciences humaines, cherchant ainsi à contribuer à l’émergence d’un dialogue interdisciplinaire autour de la centralité de la nostalgie dans les musiques populaires.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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".