Musical Setting Creation for a Yeats Poem: An Autoethnography with the Propeller Model Approach
Bibliographic record
Abstract
Researchers and artists alike have their own unique challenges when it comes to engaging with their craft for the purposes of producing a final product. Both occupations hold their own respective identities when it comes to the labour of their work whilst holding other cultural and linguistic identities. This retrospective autoethnography completed in the third-person narrative aims to explore the experience of creating a musical setting in terms of cultural importance and identity dynamics between artist and researcher. The research attempts to convey how the Propeller Model Approach (PMA) can serve as a theoretical framework by deductively providing emotive codes for the memories analyzed. The researcher’s experiences of creating a musical setting for the WB Yeat’s poem “The Song of Wandering Aengus” are recalled in the context of a Canadian Irish recording artist. The secondary literature explores human creativity, philosophy of music and other autoethnographies in conjunction with the four thematic areas of PMA. The results from the research inform of a departure from an intellect-centric view on creativity and provide insight into the music making process.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".