An Introduction, Review, and Conceptual Analysis of Mattering as an Essential Construct and an Essential Way of Life
Bibliographic record
Abstract
While the importance of having self-esteem is widely recognized and has been studied extensively, another core component of the self-concept has been relatively neglected—a sense of mattering to other people. In the current article, it is argued that mattering is an entirely unique and complex psychological construct with great public appeal and applied significance. The various ways of assessing mattering are reviewed and evidence is summarized, indicating that mattering is a vital construct in that deficits in mattering are linked with consequential outcomes at the individual level (i.e., depression and suicidal tendencies), the relationship level (i.e., relationship discord and dissolution), and the societal level (i.e., delinquency and violence). Contemporary research is described which shows that mattering typically predicts unique variance in key outcomes beyond other predictor variables. Mattering is discussed as double-edged in that mattering is highly protective but feelings of not mattering are deleterious, especially among people who have been marginalized and mistreated. The article concludes with an extended discussion of key directions for future research and an overview of the articles in this special issue. It is argued that a complete view of the self and personal identity will only emerge after we significantly expand the scope of inquiry on the psychology of mattering.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".