Bibliographic record
Abstract
It has been my hope that this thesis would serve as a bridge between three things: my past wilderness experiences, my present explorations of great nature poets, and my future as a writer. I desired to write authentic wilderness poems that gave readers new experiences, yet I was afraid that they might not be broad enough in scope and have too much sentimentality to be effective. To find a path through this dilemma I looked to great nature poets, both American and Canadian, as I sought to see how they were such successful writers. In looking at their work I asked many questions. Where did they get their inspiration? Did they use experiences or did they just write creatively? How did they talk about their past effectively? Did "place" play a large role in what they wrote about? The act of writing poetry often feels like a solitary task, as if no one has ever written like you have before, but as I searched the lives of poets I found a companionship and association that was inspiring. Looking at Margaret Atwood, for instance, gave me courage to keep alive the memories of when I was a small child in British Columbia, for she herself wrote about her own childhood experiences. John Haines was another poet who contributed to my writing process. He was not someone who simply experienced nature in his childhood. He was a man who sought it out as an adult and excluded civilization from his life. The end result of my thesis was more than I hoped for. Just by learning from great writers I was able to write boldly about my past, and I found that intertwined in my memories were people that shared those experiences with me, and they too added to the depth of my poems I call "Canadian Wild."
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.025 | 0.009 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.065 | 0.011 |
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".