Hands-On: Curricular Bridging Concepts from Maker Spaces Re-Turning to Hand-Made and Many Hands Making Together
Bibliographic record
Abstract
Poet, author and activist Maya Angelou once told Oprah Winfrey, "Do the best you can until you know better. Then when you know better, do better" (Winfrey, 2011, 2:08). This is practical advice for anyone, including curriculum developers. Angelou was, of course, speaking of forgiveness, fortitude, perseverance and learning. What is interesting for me as an educator is how Angelou went about teaching Winfrey this lesson. Recognizing the power differential between her and the then young Winfrey, she did not begin their relationship by giving advice. She began it by making Winfrey food—a making gesture signifying Winfrey’s equality with the maker. After their meal, Angelou encouraged further communion by sharing poetry made by Paul Laurence Dunbar. The making of things, physical things as in a meal, and/or artistic things as with poetry, is not featured in most Canadian curriculums. Where it does appear, it is framed as optional or vocational; making is something for those with less cognitive aptitude or for artists with marginal value to community commerce. This orientation in curriculums has perhaps led to the under-valuation of “making” as a strategy for relationship building, for reconciliation and for bridging social power divides. My presentation describes a critical discourse analysis I conducted of this popular Angelou quotation and its historical context. I use it to explore insights related to how divides might be bridged through a curriculum that assigns greater value to “making” (Fairclough et al., 2014; van Dijk, 2001; Wodak, 2011; Wodak & Meyer, 2008; Wodak & Reisigl, 2006).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.033 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".