Educators working together: Listening to children’s voices and stories about cultural and family artifacts during pandemic teaching
Bibliographic record
Abstract
This paper is located within a larger study of children’s voice and storytelling. The focus is on how children use artifacts, such as special objects and photographs, to tell stories about their lives. We studied the collaborative learning of educators, in two schools in Eastern Canada, as they used sharing circles and multimodal pedagogies, and worked to elevate and listen to children’s voices during a period of pandemic teaching. This study examines children’s things/artifacts as material culture and relates things/artifacts to artifactual literacies. The action research design included a consideration of children’s voice in early years research alongside the collaborative professional development inquiry undertaken by educators in the study. An analysis of key findings as they relate to evolving pedagogies, including how artifacts were used to tell stories, and how voice can be viewed through this artifact sharing is presented. We argue that building voice and collaboration can result from pedagogies of classroom sharing and listening. Educators’ challenges in this research and their classroom teaching during a constantly shifting set of teaching conditions are fore fronted. Insights from children’s particular artifacts and their stories enhanced educator and peer awareness of difference, and of cultural practices in families. Finally, implications for practice, and future research possibilities are presented, along with an argument for viewing children’s voice as emergent alongside classroom multimodal pedagogical practices that augment children’s voices.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".