Towards kinship literacies: attending to place relations in a play-based library program
Bibliographic record
Abstract
Kinship relationality understands the world as alive and sentient, and positions humans as participants within interconnected ecological networks. Inherently, this worldview can challenge how many educators and learners have been trained in extractive, transactional, and sedentary cultures; that is, separated out of relationship. This article shares the work of a large urban public library in western Canada, and their ‘Play Professor,’ as they built and facilitated an outdoor play-based literacy program. Following the program, amid subtle and stark reminders of ecological disaster, we noted the possibilities and complexities when moving literacy learning outdoors. Guided by kinship relationality [Donald. 2021. “We Need a New Story: Walking and the wâhkôhtowin Imagination.” Journal of the Canadian Association for Curriculum Studies 18 (2): 53–63. https://doi.org/10.25071/1916-4467.40492], this article centres how conceptions of literacies might be expanded and nuanced to engage languages and stories already alive within the place-based ecologies where the learning occurs. Sharing vignettes of how the program unfolded, we sketch shifts in facilitation as other life forms came into the storied play. To support relational renewal in and through literacy practices we then bring forward the notion of kinship literacies and articulate three catalysts to support its growth: attuning to ecological stories, enhancing holistic practices, and expanding dialogue.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.017 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".