A Beat of Goodbye: An Autoethnographic Account of My Last Days with Grandma
Bibliographic record
Abstract
In this paper, I use field notes, journal entries, and memory recall to write an autoethnographic account of my experiences of the last days of my grandma’s life. I use writing as method in the form of an introspective narrative, layering artistic storytelling and academic references. My original research goal was to better understand the experience of loving and caring for a very old family member by showing the inside of how I experienced my grandma’s aging and final days, including her move to a retirement home, and her death a short time later. By sharing narratively my lived experiences of my grandma’s last days, I also hoped to disrupt some of the socially accepted interpretations surrounding physical bodies and aging, especially for women. Although my initial goal was to understand how these types of experiences transform us, in the process of telling this story I found that what I also gained was a deeper understanding of who my grandma was, and ultimately, who I am.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".