Review of “Good Question: Arts-based approaches to collaborative research with children and youth” edited by Michael J. Emme and Anna Kirova (2017)
Bibliographic record
Abstract
In this edited volume published as an e-book, Good Question: Arts-based Approaches to Collaborative Research With Children and Youth, Michael Emme and Anna Kirova wonder whether communities of adult researchers, artists, educators, and youth working in collaborative and playful ways can co-construct inquiry practices which support young people to lead their own research investigations. The collection is composed of three parts. The first part, “Comics,” focuses on research methods designed for children and youth. The second and third parts, entitled “Collaborations” and “Theory” respectively, include chapters written by new and experienced researchers/scholars who elaborate examples of collaborative arts-based research with children and youth. The design of Emme and Kirova’s contribution to the literature is highly imaginative. In a time of burgeoning interest in arts-based research approaches, the volume has earned a key place. It will no doubt become an essential work for those interested in such innovative research with children and youth.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".