Rachel Gotlieb and Cora Golden, Design in Canada: Fifty Years from Teakettles to Task Chairs
Bibliographic record
Abstract
responsible for carpet production and she paints pictures and postcards. 'We don't want to be Indian or Chinese: we are Tibetan.' I cannot help but feel totally annoyed and powerless as an instructor. The information is more aggravating than useful and means more to the writer/photographer than anybody else. Why has he chosen this to highlight his meeting? What is the prognosis for this family? Are teenagers expected to understand? Is this family now squarely placed in its box of identification? What have we learned? We have learned we need to know more and go elsewhere for answers, yet if we do this for nearly 1300 images, the students will have a reading list of over 3000 books. In comparing it with the Materia] World text, the bottom line is that 1000 Families: The Family Album of Planet Earth is just that—a family album of pretty pictures to flip through. Out of context, out of place, out of time, the book fails to stimulate interest and will prove unusable for educators but stands as a fabulous record for Ommer himself to cherish and expand upon when giving the inevitably more entertaining public lecture.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.009 |
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".