Algorithms developed over time through changes in youth-material collaborations, defining learning as physical expansion of algorithmic production¶
Claim · All claims
Evidence · 1 study · qualitativer?·q2
Currency · newest study 2024 · checked against its sources 2026-10-20 (automated, 1 of 1 entry) · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? Variations of established computational repetitions added new routines, showing that the craft-specific algorithm changed over time through shifting youth-material collaborations. → Anna Keune 2024
Evidence¶
Anna Keune 2024¶
Anna Keune. (2024). Learning within fiber-crafted algorithms: Posthumanist perspectives for capturing human-material collaboration. Intern. J. Comput.-Support. Collab. Learn. https://doi.org/10.1007/s11412-023-09412-1
q2 · i? · qualitative · r?
A second coding iteration on the same video corpus analyzed variations of the baseline repetitions to answer how the algorithm changed over time, revealing "intra-actions of materials and humans" that pushed back on human intentions.
"The variations physically increased the number of routines that were possible because they added new routines. This process of doing and undoing revealed intra-actions of materials and humans"
Discussion¶
Related Claims¶
- The editorial reports that human-material interactions in fibre crafting produce algorithmic outcomes, with materials acting as collaborative agents (Keune) — a broader claim this one bears on
- In weaving, performed algorithms emerged as repeated youth-material movements one dimension at a time, producing reliable fabric outputs — related
- Fiber crafting can engage proportional reasoning through nested, personally-defined units — related
- Neither youth nor loom alone drove the algorithmic production and fabric output; both actively produced it collaboratively — related
- Computation in weaving is characterized by the performance of automation, with student-loom patterns moving from irregular to regular over time — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
algorithms-expand-over-time-youth-material-changes |
title |
Algorithms developed over time through changes in youth-material collaborations, defining learning as physical expansion of algorithmic production |
description |
Algorithms developed over time through changes in youth-material collaborations, defining learning as physical expansion of algorithmic production |
status |
review |
evidence_strength |
weak |
newest_evidence |
2024 |
source_checked |
2026-10-20 |
generated |
by process:wiki-ingest on 2026-10-18 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — anna-keune-2024 |
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