Bibliographic record
Abstract
Everything about Canadian artist Emily Carr was big. She painted huge canvases with great sweeps of movement and colour. Her subjects were the giant cedar and dense pine forests of west-coast Canada, wide expanses of sky and sea, and the exaggerated face and figure of First Nations ‘totem poles’. 1 Carr’s work was energized by an emotional personality, a forceful creative drive and an intense spiritual yearning. Even the darkest forest depths radiated light and energy. Her prose exalted in the drama of life — its joys and anguish; her language tended towards excess. Carr’s writing did not have the smooth polish of a sophisticated stylist; rather it was crafted to emulate and to evoke the ‘truth’ of life as she knew it in the rawness of being and feeling. Carr herself was round-faced, short but broad — a woman with bodily presence and an idiosyncratic style of dress that prioritized comfort (pragmatic and loose) over fashion (decorative and restrictive). She lived her life in the company of a monkey, a rat, various dogs and birds. She travelled alone into the wilderness and indigenous settlements of British Columbia and Alaska in the last decade of the nineteenth and first decades of the twentieth centuries, undaunted by her own fears or the expectations and reactions of others. For brief periods she had an old grey caravan, dubbed ‘the elephant’, towed into the depths of the forest that she might better capture the mystery of its green life-force on canvas. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.014 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".