Intergenerational Love Song: When I Say?! You Say?!
Bibliographic record
Abstract
This paper discusses Intergenerational Love Song: When I say?.! You say?.!, a project I conceived and carried out in 2017 with four adult band coaches at Girls Rock Camp Toronto. Using song writing as a mode of inquiry, participants were able to reflect on their role as mentors to young people. This project sought to disrupt the ways in which band coaches at Girls Rock Camp think about the collaborative song writing process, the act of teaching and the ways in which listening is crucial to the ways in which we respond. In so doing, this project engages with broader ideas of power, dialogue and the binaries that make their way into discourses on gender and human development. This study is unique in that it brings into conversation musicology, pedagogy and girlhood studies in a grounded project that makes room for listening. Note: It is recommended to listen to the songs in conversation while reading this project report and specifically during the sections that analyze each song. Big Beef! Hard Life! https://girlsrocktoronto.bandcamp.com/track/rogue-emotions (I really love this) Big Beef! Hard Life! https://soundcloud.com/magali-meagher/i-really-love-this-big-beef-hard-life/s-ZOCJ4?
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".