Self as both target and judge: Who has an easier time knowing their own personality?
Bibliographic record
Abstract
OBJECTIVE: The past two decades have established that people generally have insight into their personalities, but less is known about how and why self-knowledge might vary between individuals. Using the Realistic Accuracy Model as a framework, we investigate whether some people make better "targets" of self-perception by behaving more consistently in everyday life, and whether these differences have benefits for psychological adjustment. METHOD: Using data from the Electronically Activated Recorder (n = 286), we indexed self-knowledge as the link between self-reports of personality and actual daily behavior measured over 1 week. We then tested if consistency in daily behavior as well as psychological adjustment predicted stronger self-knowledge. RESULTS: We found that behaving more consistently in everyday life was associated with more accurate self-reports, but that psychological adjustment was not. CONCLUSIONS: Analogous to interpersonal perception, self-knowledge of personality might be affected by "target-side" factors, like the quality of information provided through one's behavior. However, unlike being a good target of interpersonal perception, self-knowledge does not seem to be related to psychological adjustment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".