Disney And The Magical World Of Writing; How Combining Creativity With Learning Disabilities Can Promote Academic Success
Bibliographic record
Abstract
Through a Disney perspective, this author discusses how students can use creative strategies to cope with learning disabilities in secondary, post-secondary and even graduate levels of academic achievement. In particular, the paper will be presenting how the author, who has an infinity for “everything Disney”, chose to use both Disney Characters and Disney Song titles from movies and television shows, as a creative strategy in the organization of her master’s research thesis. The research study entitled “Why is it so hard to go a good thing? The Paradox and Dilemma of Parental Advocacy within the Individual Education Planning Process” took a qualitative, phenomenological approach to investigate the experiences of parental advocacy and to seek out macro/micro factors that may have contributed to positive or negative outcomes within the IEP process. The author used Disney song titles as an adaptive tool not only to help in the organization of the findings of the research, but also to help illuminate the phenomenological existential themes that were revealed through the analysis. The paper hopes to demonstrate that through the use of creative strategies in otherwise conventional academic expectations, students experiencing disabilities may increase the potential of achieving academic success.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".