Death and Black Diamonds: Meaning, Mortality, and the Meaning Maintenance Model
Bibliographic record
Abstract
The Meaning Maintenance Model (MMM; Heine, Proulx, & Vohs, 2006 Heine, S. J., Proulx, T. and Vohs, K. D. 2006. The Meaning Maintenance Model: On the coherence of social motivation. Personality and Social Psychological Review, 10(2): 88–111. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) proposes that human beings innately and automatically assemble mental representations of expected relations. The sense of global meaning that these relations provide is regularly disrupted by unrelated or unrelatable experiences, which elicit feelings of meaninglessness. People respond to these disruptions by engaging in meaning maintenance to reestablish their sense of symbolic unity. Meaning maintenance often involves the compensatory reaffirmation of alternative meaning structures through a process termed fluid compensation. The MMM proposes a fundamental reinterpretation of the social psychological literature, arguing that meaning maintenance is a general mechanism that underlies a host of diverse psychological motivations, including self-esteem needs, certainty needs, and the need for symbolic immortality. In particular, the MMM stands in contrast to Terror Management Theory in that mortality salience is explained by the MMM to be one of many specific instantiations of threats to meaning that engenders fluid compensation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".