Relational Spending in Funerals: Caring for Others Loved and Lost
Bibliographic record
Abstract
Funeral rituals perform important social functions for families and communities, but little is known about the motives of people planning funerals. Using mixed methods, we examine funeral planning as end‐of‐life relational spending. We identify how relational motives drive and manifest in funeral planning, even when the primary recipient of goods and services is dead. Qualitative interviews with consumers who had planned pre‐COVID funerals ( N = 15) reveal a caring orientation drives funeral decision‐making for loved ones and for self‐planned funerals. Caring practices manifest in three forms: (a) balancing preferences between the planner, deceased, and surviving family; (b) making personal sacrifices; and (c) spending amount (Study 1). Archival funeral contract data ( N = 385) reveal supporting quantitative evidence of caring‐driven funeral spending. Planners spend more on funerals for others and underspend on their own funerals (Study 2). Preregistered experiments ( N = 1,906) addressing selection bias replicate these results and find generalization across different funding sources (planner‐funded, other‐funded, and insurance; Studies 3A–3C). The findings elucidate a ubiquitous, emotional, and financially consequential decision process at the end of life.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".