Doing pedagogical intentions with <i>Facetiming Common Worlds</i> (and Donna Haraway)
Bibliographic record
Abstract
Working with stories of children’s relationships with place and technologies from an early childhood education pedagogical inquiry research project in Melbourne, Australia and Victoria, Canada, this article takes up the concept of “pedagogical intentions” to consider how educators and researchers might cultivate intentional teaching practices relevant to the complex worlds we inherit with children. We think with a common worlds pedagogies approach to extend conceptualizations of intentional teaching held in dominant Euro-Western early learning frameworks in Melbourne and Victoria. After situating our understanding of pedagogical intentionality as an ongoing, purposeful, answerable practice of shaping and caring with everyday pedagogical relationships, we share three stories of how we activate our Donna Haraway–inspired intentions with children. By questioning how our pedagogical intentions inform our work, we assert that sharing and putting at risk our intentions is a necessary practice for thinking collectively with children, more-than-human others, and technologies within early childhood education.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".