Bibliographic record
Abstract
The paid obituary phenomenon has generated a fresh source of revenue for newspapers throughout the United States and Canada. Its remarkable growth has occurred in tandem with the renaissance of editorial obituary columns over the past 20 years. Some spectacular sums of money are expended on this facet of classified advertising; largesse, however, does not necessarily endow the practice with quality. It is not subjected to the rigour of house editorial style, it does not have to conform to the same code which is applied to display advertising, it can engage in whimsy and excessive sentiment, and its content often stretches both credulity and truth. Historians drawing on this soft underbelly of the newspaper obituary art will find their source material compromised by elision and fabrication. Yet, with classified revenue so important to newspapers everywhere, proprietors outside the United States and Canada could well be seduced by the financial return that this practice offers. It has the capacity to address a belief, expressed by the American journal U.S. News & World Report, that extending the classified agenda might be the only means by which some newspapers will avoid following their readers to the grave. Editorial integrity and advertising opportunity, accordingly, are subject to the potential for a new strain of confrontation.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.797 | 0.696 |
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".