Bibliographic record
Abstract
Music influences young children’s development and learning in many ways. Music as a communicative tool is a natural way to express emotions and thoughts (Yazejiian & Peisner-Feinberg, 2009). Not only is music intrinsically fun and motivating for young learners, it helps improve children’s language, literacy, and communication skills (Paquette & Rieg, 2008). Further, it helps promote children’s creative inquiry and expression, and their aesthetic and holistic development as human beings. The above musical outcomes are achievable, notwithstanding the level of musical training or the ability of the teacher. There is incredible value in teachers’ integration of music activities into daily practices in today’s diverse early childhood classrooms. In this research project, I explore these propositions using narrative inquiry. I share stories of my personal and professional music experiences and, drawing from these stories, I reflect on my own teaching practices and how these experiences have shaped me as an early childhood teacher. I support and connect my narratives to literature written by early childhood educators and music specialists. I provide a review of literature on the benefits of music in early learning environments, a review that provides theoretical and curricular support for early childhood educators to enhance their play-based pedagogy with musical activities. Through the use of video segments, I demonstrate and describe ways to incorporate music into routine early learning environments. The viewer then can see for himself/herself the in-class dynamics and form his/her own impressions. This resource provides teachers with ideas, leads, resources, and hands-on activities that will assist them in integrating play-based music activities into daily classroom practices. Normal 0 false false false EN-CA JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:Table Normal; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri,sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-fareast-language:EN-US;}
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".