Bibliographic record
Abstract
My mother’s love of Tootsie Rolls was the only fact I could grasp after her sudden passing. I wanted to share this and other memories of her through a eulogy that was whimsical, far-ranging, and entertaining, but I struggled to write one. My struggles reminded me of other writing challenges, such as my recent dissertation proposal, although there I was partly guided by my arts-informed research methodology framework. Gradually, I found some of those methodological elements could illuminate parts of eulogy writing: formal concerns, audience, presence and engagement, subjectivity, and meaning-making all resonate with arts-informed research’s commitment to form, audience, creative enquiry, researcher presence, and holistic quality. These connections show arts-informed research affords lifelong learning opportunities apart from academic practice; in this case, arts-informed research is a resource tool for navigating lived experiences of grief and grief writing. Moreover, arts-informed research encourages affective narratives and socially-constructed meanings to produce new understandings, which I realize here by including eulogy excerpts to produce an artistic representation of “research” about my mother (including her undying love of chocolate).
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.218 | 0.086 |
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".