Who Knows Best What the Next Year Will Hold for You? The Validity of Direct and Personality–based Predictions of Future Life Experiences across Different Perceivers
Bibliographic record
Abstract
This study explored the validity of person judgements by targets and their acquaintances (‘informants’) in longitudinally predicting a broad range of psychologically meaningful life experiences. Judgements were gathered from four sources (targets, N = 189; and three types of informants, N = 1352), and their relative predictive validity was compared for three types of judgement: direct predictions of future life experiences (e.g. number of new friendships), broad (Big Five) domains (e.g. extraversion), and narrower personality nuances (e.g. sociable). Approximately 1 year later, the targets’ actual life experiences were retrospectively assessed by the targets, and by informants nominated by the targets (TNI). Overall, we found evidence for predictive validity across predictor sources and types. Direct predictions by targets were by far the most valid, followed by TNI. Personality–based predictions by targets and TNI had substantial but lower validity. Domain–based predictions were less valid than nuance–based predictions. Overall, informants with lower ‘liking’ and ‘knowing’ towards targets made less valid predictions. Person–centred multilevel analyses showed both considerable validity of direct predictions (which increased with knowing) and positivity bias (which increased with liking). Taken together, given the relatively high methodological rigour of the study, these results provide an especially realistic picture of the rather moderate predictive power of person judgements regarding future life experiences and corroborate the common practice of obtaining such judgements from targets and their close acquaintances. © 2020 The Authors. European Journal of Personality published by John Wiley & Sons Ltd on behalf of European Association of Personality Psychology
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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 | 0.025 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".