Humanizing Market Relationships: the DIY Extended Family
Bibliographic record
Abstract
Abstract Purpose: Market logics have increasingly dominated consumer life worlds. Consumers may embrace marketization, or they may resist it, try to escape it, rebel against it, or actively manage its effects. This chapter examines the marketization of elderly care (in the form of transactional service provider relationships) and how consumers apply humanizing strategies to market relationships. Methodology/Approach: This is a qualitative interpretive study using in-depth interviewing, observations, and the analysis of media coverage. Findings: Drawing on institutional theory, this study shows how consumers humanize a marketized service relationship by weaving social logics into existing market logics. Our research finds consumers engaging in three humanization strategies: (1) moving beyond transactional relationships; (2) sharing consumption experiences; and (3) reinforcing social bonds through giving. The end result is the do-it-yourself (DIY) creation of extended family relationships from market resources. Social Implications: The context of this study is a government-supported, non-profit, exchanged-based retirement support scheme that addresses the challenges of global population aging and the increasing anonymization and estrangement in our society. The authors tentatively suggest that our findings represent a move to mitigate adverse effects of neoliberalism. Originality/Value of the Paper: Prior research has shown that consumers embrace marketization, resist it, try to escape it, rebel against it, or actively manage its effects. The authors identify another strategy used by consumers to address the increasing marketization of their life worlds, namely humanization. This study shows that consumers assemble market resources and humanize transactional service provider relationships by weaving social- into market logics resulting in the creation of a DIY extended family.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".