A love letter to ironing: Learning and unlearning
Bibliographic record
Abstract
What does ironing have in common with learning to build a digital world? This photo essay explores the nature of learning and unlearning through the juxtaposition of skills, specifically ironing, a competency acquired for the most part through unconscious absorption, vs. creating in a digital medium where our learning was much more self-conscious. In learning to build and programme in Unreal Engine (UE5), a game engine capable of enabling a virtual reality (VR) experience, we learned, once again, what it means to learn. The photo essay is written in a lyrical style to encompass both the prosaic and poetic ways that we engaged with a project titled, Craft & The Digital Turn (CDT). By using VR as a means of data visualization we sought to bring our craft backgrounds together with future trends in digitalization and communication. Through personal narratives and histories, melded with theory and analysis, we hope to record a process that was deeply engaging and extremely challenging for us as practitioners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".