The nature of the highly artistic student in visual arts at secondary school
Bibliographic record
Abstract
This qualitative investigation examined the nature of 7 highly artistic visual arts \nstudents at 2 secondary schools in southcentral Ontario. Through interviews, \nquestionnaires, observations, and artwork documents, this study attempted to understand \nthese highly artistic students in terms of creativity, motivation, social and emotional \nperspectives, and cognitive processes. Data collection occuned over a 3-monlh period. \nand the data analysis program NVivo 7 was used for coding to develop themes and \ncategories for organizing data. \nThe findings of this study illustrate the significant place that \\ isual arts can lake in \nthe growth and development for the youth of today. Participants idcniificd dcxclopnig \ncritical thinking and problem-solving skills, taking risks, and meeting challenges ilirouuh \ntheir engagement in the creative process. The transferability of these skills \\\\ as \nreferenced to numerous aspects of their lives. By enhancing individual perspectives \nthrough the study of visual arts, their local and world connections were extended, and \nenvironmental and societal concerns evolved. In addition, the communicative \nopportunities that visual arts provided for these students in terms of personal expression \nprovided emotional health and paths of personal discovery. \nThrough the participants' production of artwork with the many stages this \ninvolves, combined with insight into their needs, the participants relayed miportant \nsuggestions for programming enhancements and educational settmgs lor \\ isiial arts \nclassrooms. These suggestions are meaningful for educators and curriculum developers \nof the future.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".