The word ‘craft ’ is, like so many important words in English, brief, pungent and ambiguous.
Bibliographic record
Abstract
Edward Lucie-Smith’s quote, while taken entirely out of context, is well-suited for looking at craft within a post-secondary environment. The fact that craft is both brief and ambiguous word is generally understood---the compact term has long been open to both illustrious and embarrassing connotations. Its pungency is perhaps lesser known. While some may understand this as an evocative perfume of rich sensory experience, within the context of higher education, the pungency of craft is more likely to be a strong and fairly disagreeable odour. I am interested in looking at the word craft through the specific lens of institutional learning, and to give some consideration to the following questions: What are some of the overarching tensions that invite closer scrutiny when looking at craft within higher learning? Do these tensions offer new opportunities for rethinking how craft programs are taught or institutionally positioned? And what role does language play in our analysis? My vantage point on these questions is anchored in the field of textiles, and is chiefly based upon the college/university system in Canada. While I appreciate that significant differences may exist both within and beyond our borders, many of the issues likely have broader
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".