Refiguring the Positioning Through Tabletop Game Redesign: What it Means to Engage in Culturally-Sustaining Learning as a Family
Bibliographic record
Abstract
In this paper, we present our approach to culturally sustaining learning, enabling the contribution of non-dominant voices and cultural resources to collective learning activities.In our study, we proposed activities for families to redesign tabletop games with ideas, categories, and processes that reflect their interests and culture on their own time during the global pandemic.We collected data through online video communications and families sharing their own artifacts (e.g., photos, videos, and blogs).We describe how families expressed what matters to their members individually and collectively and how this was intertwined with shifting family members' relational positions. I don't think it was as easy to relate to what was going through the minds of each creator of the game pieces…all of us had probably very different perspectives. Even within a family unit, it is probably really hard to even get on the same page because sometimes I am like, I don't get it, what is going on?The mother of Family 1, during the final interview
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".