Groupsourcing Folklore Sound Files: Involving the Community in Research
Bibliographic record
Abstract
Digital technologies make possible new ways of managing folklore field recordings. Two programmers, a graduate student, and the author developed a database that allows the user to go directly to the point in a sound file where a particular topic is discussed. This is a research tool and the task here was to create a modified site for the general public. The technique used was crowdsourcing, asking the public to transcribe and translate songs, stories, and accounts of belief. The project revealed how heritage issues affect public participation. People who expressed initial enthusiasm were reluctant to participate because they were timid about their language knowledge. Paradoxically, formal instruction leads to the timidity we observed. People who did contribute to our project transcribed and translated songs only. Language is retained in song even as it is lost elsewhere. Songs are also familiar material, associated with the past. Contributions driven by interest in new material directly from Ukraine did not materialize. A romanticized image of the past and suspicions that Ukraine has been Sovietized and Russified encourage preservation of the old and work against interest in the new.
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 | 0.029 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".