Bibliographic record
Abstract
This paper explores how grief is intertwined within artificial intelligence (AI) and other digital areas. It examines concepts such as the griefbot, an AI used to provide communication between the deceased and the bereaved, digital online memorial spaces to commemorate those who have passed, and digital immortality. While griefbots provide comfort to those who have lost a loved one, questions surrounding ethics of use, such as obtaining the consent of the deceased, using the deceased’s data, and respecting their privacy, remain relevant. The digital afterlife industry, which includes online memorials, puts into question several societal challenges. These challenges can lead to debates over who “deserves” the most to have access data and digital spaces. Capitalism and digital immortality may reveal power dynamics with the deceased. For instance, business leaders and public figures may leave behind a digital legacy to continue to wield authority beyond the life of their physical bodies. As societies continue to merge aspects of human lives (and deaths) into the digital world, we must address issues of consent, privacy, and equitable access. Grieving and remembrance must not be lost in the digital age.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".