Self-discrepancies in mind perception for actual, ideal, and ought selves and partners
Bibliographic record
Abstract
Defining and measuring self-discrepancies in mind perception between how an individual sees their actual self in comparison to their ideal or ought self has a long but challenging history in psychology. Here we present a new approach for measuring and operationalizing discrepancies of mind by employing the mind perception framework that has been applied successfully to a variety of other psychological constructs. Across two studies (N = 265, N = 205), participants were recruited online to fill in a modified version of the mind perception survey with questions pertaining to three domains (actual, ideal, ought) and two agents (self versus partner). The results revealed that participants idealized and thought they ought to have greater agency (the ability to do) and diminished experience (the ability to feel) for both themselves and their partner. Sex differences were also examined across both studies, and while minor differences emerged, the effects were not robust across the collective evidence from both studies. The overall findings suggest that the mind perception approach can be used to distill a large number of qualities of mind into meaningful facets for interpretation in relation to self-discrepancy theory. This method can breathe new life into the field with future investigations directed at understanding self-discrepancies in relation to prosocial behaviour and psychological well-being.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".