Bibliographic record
Abstract
My brother was diagnosed with cancer in early July, 2017. He died on August 22, 2017. I have written many poems about growing up with my brother, and now that he has died, I am revisiting the poems I once wrote and writing more because writing is my way of addressing grief. Writing is an integral path in the curriculum of loss, and I trust writing will lead me to the understanding I need to begin each new day with hope, even joy in the midst of loss. Joy Kogawa (2016) sees “the world as an open book embedded with stories” that we can hear “if we have ears to hear” (p. 149). When my brother died, the loss was grievous, but the loss reminded me I am alive and I must keep on telling stories. I am learning to live with the curriculum of loss. As one who is left behind, my calling is to remember my brother and to share stories about him, but my calling is also to explore connections between life and loss, and the possibilities that extend beyond loss. Ultimately the curriculum of loss is a curriculum of hope. I want to be open to learning from my brother. I am not satisfied with remembering or memorializing him. I want to continue in a pedagogic relationship with my brother so that I learn from both memories and loss, as well as from the possibilities that continue.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.023 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.010 |
| 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".