WHEN MATTER IN THE CLASSROOM MATTERS: ENCOUNTERS WITH RACE IN PEDAGOGICAL CONVERSATIONS
Bibliographic record
Abstract
This article considers how mattering and meaning are mutually constituted in the production of knowledge (Barad, 2007). Drawing on a research project with first year early childhood education (ECE) students in a university setting, I argue that material-feminism, as understood through the work of Barad (2007, 2008), offers a lens through which pedagogical practices can be re-conceptualized as more than anthropocentric endeavours. The research project explores the processes that occurred when a group of ECE students and I engaged with and in pedagogical narrations over one academic term as we attempted to make visible and disrupt the hegemonic images we held of both children and childhood. In the doing of pedagogical narrations, artefacts were produced that were not merely representations of our collaborative thinking. Rather, the artefacts that emerged-in between the material, the discursive and the participants, were themselves agentic; they invited us to shift our gaze and our conversation, and thereby new meanings and realities were produced. I provide one example that discusses how the presence of the artefacts invited “race” into a conversation of childhood in a way that reverberated in our thinking, feeling, and being. The article concludes by considering the pedagogical implications for learning, for both children and those learning to work with children, when matter comes to matter in the classroom.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.033 | 0.053 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".