Is Life Getting Better and Better or Worse and Worse for Oneself and Others? Investigating the Link Between Beliefs About Life Unfolding Over Time and Motivation for a Positive Future
Bibliographic record
Abstract
ABSTRACT We report two preregistered studies examining how individuals view life to be unfolding over time for themselves, people in their community, country, and all of humanity. We evaluated the link between such beliefs and “well‐doing,” that is, the motivation to engage in actions geared toward an improved future. In Study 1 (N = 963; M age = 40.83 years; 48.2% female), individuals reported their beliefs about how life is unfolding over time for people in one of four target conditions: self, community, country, or all of humanity. In Study 2 (N = 947; M age = 39.52; 51.4% female), individuals were randomly assigned to one of three narrative direction conditions (better, stable, worse) for one of the four target conditions. In Study 1, participants viewed life as getting better over time for the self, on average, but getting worse for the other targets. In both studies, perceiving life as improving (vs. worsening) was associated with stronger well‐doing intentions, particularly in the self condition in Study 1 and with respect to participants' general motivation in Study 2 regardless of target condition. Thus, viewing life as getting better and better (vs. worse and worse) over time may play an important role in motivating individuals to strive toward making life better, not only for oneself but also for people in one's community, country, and all of humanity.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".