Remembering the Dead: What Community Newspaper Memorials Reveal
Bibliographic record
Abstract
People will understandably continue to remember significant persons long after their deaths. One possible remembrance practice is the placement of a memorial about the deceased person in a community newspaper. It is not clear what these memorials are intended to do, how they are constructed, who places them in a public sphere for open viewing, and what purpose or purposes they serve. As these memorials could be important for grief management and other personal, family, or social purposes, an examination of memorials to the dead appearing over one year in the Edmonton Journal, the primary newspaper for a Canadian city of one million inhabitants, was conducted. This research project found memorials were uncommon (N = 567) compared to obituaries (N = 4,865), and very uncommon in relation to the number of decedents who could have been memorialized. Memorial authors were most often parents or children, with memorials usually appearing on a second year or later death anniversary. Two content themes were identified: (a) enduring love for the deceased, and (b) a continuing if not permanent remembrance of them. The findings raise many questions, but primarily how people can openly and constructively grieve long after the death of a loved one.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".