Improving writing through musical strorytelling strategies
Bibliographic record
Abstract
In this study, I facilitated and observed five lower mainland classrooms in British Columbia Canada employing an unmeasured intervention to improve writing efficacy in grade four students through musical storytelling strategies. The original intervention I developed and will be describing is an inclusive design for general classroom learning, fusing two Canadian public design approaches – Stanley King’s (Youth Manual Co-Design) and Murray Schafer’s (World Soundscape Project, Composer in the Classroom). Over one thousand writing samples from the five classes in the study were collected and scored by two Inter-Observers using a Likert-scale measurement instrument. Inter-observer agreement for all selected samples from each of the five classes measured 81.4% and higher. A lagged, multiple-probe design was employed to model raw data comprising some eighty-five thousand entries. The five classes showed statistically significant results with a combined mean gain of 3.99 out of 36 with a p-value < 0.001. The lowest quartile average in the five classes also showed statistically significant results with a combined mean gain of 6.51 out of 36 with a p-value of < 0.001.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".