Bibliographic record
Abstract
Last year, I was grieving the loss of my dad’s little brother, my Uncle Kumar. I wrote this poem the night that I attended his memorial service over Zoom. My uncle will always have a special place in my heart. I moved to New Zealand from Malaysia in 2004 when I was 14 years old. A year later, my parents and my little sister followed. Every visit back home to Malaysia for the next 15 years, Uncle Kumar would be there to greet us at the airport. His familiar face would be the first to greet us when we landed, and the last to see us off when we left. I miss the feeling of seeing his warm smiling face in the KL airport’s arrival area. Climbing into the familiar comfort of his car in Malaysia’s thick humid air was always my first warm welcome home. In this poem I remember my Uncle Kumar, with whom I enjoyed sharing space with in both conversation and in quiet; The quiet tears he tried to hide while driving me back to the airport at the end of my visits home; The quiet meals I shared with him while scrolling through my phone; The quiet drives home during my childhood, when my parents were busy at work and weren’t able to pick my sister and I up from school that day. He loved being our uncle. He never outright said so, but he showed us how much he did. Uncle Kumar always showed up and I am so deeply blessed to have known and loved him. He is a person I learned so much from, and whose demeanour and temperament I so often see in myself. My world changed when you left us, and we miss you so much.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.177 | 0.210 |
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".