In amongst the glitter and the squashed blueberries: crafting a collaborative lens for children’s literacy pedagogy in a community setting
Bibliographic record
Abstract
In this article, we bring together relational arts practice (Kester, 2004) with collaborative ethnography (Campbell and Lassiter, 2015) in order to propose art not as a way of teaching children literacy, but as a lens to enable researchers and practitioners to view children’s literacies differently. Both relational arts practice and collaborative ethnography decentre researcher/artist expertise, providing an understanding that “knowing” is embodied, material and tacit (Ingold, 2013). This has led us to extend understandings of multimodal literacy to stress the embodied and situated nature of meaning making, viewed through a collaborative lens (Hackett, 2014a; Heydon and Rowsell, 2015; Kuby et al, 2015; Pahl and Pool, 2011). We illustrate this approach to researching literacy pedagogy by offering a series of “little” (Olsson, 2013) moments of place/body memory (Somerville, 2013), which emerged from our collaborative dialogic research at a series of den building events for families and their young children. Within our study, an arts practice lens offered a more situated, and entwined way of working that led to joint and blurred outcomes in relation to literacy pedagogy.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.013 | 0.039 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".