Bibliographic record
Abstract
Dr Phyllis Silverman, a pioneer in the field of death and bereavement, died in Lexington, Massachusetts, on 10 June 2016. A tiny, fit, silver-dark-haired woman, who swam almost every day of her adult life until she became ill, was a forceful, perceptive and outspoken proponent of valuing and listening to the expertise of the bereaved and of learning from them. In the course of her learning, she wrote or co-authored twelve books, fifty book chapters and thirty-five first and co-authored articles, and served as a Professor at the MGH Institute of Health Professions, a Scholar in Residence at the Women’s Studies Research Center, Brandeis University and as a founder of The Children’s Room in Arlington, MA, where she taught, supervised and applied the theories that she developed. She was a Co-Principal Investigator in the Child Bereavement Study at the Massachusetts General Hospital, the winner of multiple awards for her groundbreaking work on children’s grief, books on widows and widowers, and on processes of grief, and she enjoyed an international reputation, teaching in Israel, Canada, Australia, Hong Kong, South America and many other places.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.479 | 0.321 |
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".