Escape Theory and Materialism: An Experimental Paradigm for Self-Blame
Bibliographic record
Abstract
Escape theory, as proposed by Donnelly and colleagues, identifies six steps that contribute to materialistic behaviour supported by research. Self-blame, the second step, is the most novel component and thus least investigated. In addition, research in materialism is primarily correlational. The present study investigates the second step, self-blame, with an experimental design. A materialist university sample (N = 56) was randomly assigned to a control or self-blame vignette. The self-blame vignette was novel in nature, utilizing the illusion of a choice-based narrative. Participants then engaged in a shopping simulator comprised of products ranging in materialistic value. A subsequent questionnaire then assessed materialistic behaviours and associated cognitions of interest. ANOVAs were then conducted to test between-group differences in behaviour and cognitions, while bivariate analyses tested correlations between such variables. ANOVAs revealed that inducing self-blame caused materialists to be happier with their purchases after a shopping experience than control materialists. Meanwhile, the bivariate analyses extend literature by providing experimental evidence for materialistic behaviour and associated cognitions, including materialists experiencing greater guilt after purchasing more products and spending more money than non-materialists. Future research can continue to enhance the overall effectiveness of self-blame induction methods. Moreover, researchers can also investigate the effect that reducing self-blame has on halting the progression through the six steps of materialistic behaviour proposed in Escape theory. This, in turn, can enhance materialists’ wellbeing. Discipline: Psychology (Honours) Faculty Mentor: Dr. David Watson
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".