Bibliographic record
Abstract
Five years ago, I began a quest for enjoyment which has evolved and expanded into an epic journey. A ukulele for X-mas quickly gave way to the guitar. While attaining a degree as a sociologist, at 55 years of age, I blossomed to become a musician, poet, author, artist, and performer. With nearly 160 completed pieces of music alone, I have amassed a portfolio any artist would be proud to have. I have composed songs for a Metis language webpage, and painted a wall mural at MacEwan University. I have had song lyrics and stories published. I hold my own on the same stages with some of Edmonton’s finest musicians and poets. Inspiration comes from life’s many ups and downs. I dare to call myself a prodigy since I am self-taught in all my art. However, currently, I am in my 3rd year of psychology, at MacEwan University for a 2nd degree, allowing me to practise music/art therapy. Sweet Baby is a song about anticipating the birth of my first grandchild, yet written through the eyes of the mother to be. Epic Odyssey is my story, events of my life Who Needs TV is also about my life, alcoholism, and family. So Shy is about watching the reactions of children to my busking in the LRT (light rail transit) station in Edmonton, Alberta. The Day My Boyfriend Died, indeed humor, was a black thought put into prose.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.103 | 0.049 |
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".