The Anti-Mattering Scale: Development, Psychometric Properties and Associations With Well-Being and Distress Measures in Adolescents and Emerging Adults
Bibliographic record
Abstract
Previous work has focused on positive feelings of mattering, which pertain to the human need to feel significant. In the current article, we examine a complementary yet distinct construct involving feelings of not mattering that may arise from being marginalized and experiences that heighten a sense of being insignificant to others. We also describe the development, validation, and research applications of the Anti-Mattering Scale. The Anti-Mattering Scale (AMS) is a five-item inventory assessing feelings of not mattering to other people. Psychometric analyses of data from samples of emerging adults and adolescents confirmed that the AMS comprises one factor with high internal consistency and adequate validity. Our findings suggest that individuals who feel like they do not matter to others have a highly negative self-view, insecure attachment, and perceived deficits in meeting key psychological needs. Analyses established that links between elevated AMS scores and levels of depression, social anxiety, and loneliness. Most notably, scores on this new measure predicted unique variance in key outcomes beyond the variance attributable to other predictors. Overall, these results attest to the research utility and clinical potential of the AMS as an instrument examining the tendency of certain people to experience a profound sense of not mattering to others in ways that represent a unique source of risk, social disconnection, and personal vulnerability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".