HOW GOAL REPRESENTATIONS MODULATE ASSOCIATIONS BETWEEN EVERYDAY AFFECT AND GOAL PURSUIT AMONG OLDER ADULTS
Bibliographic record
Abstract
Abstract Individuals differ in the extent to which they represent their goals as hoped-for versus feared states. We examined the role of such goal representations for how everyday affective experiences and goal pursuit are intertwined. When goals are represented as hoped-for states, we expected stronger associations between daily positive affect and goal pursuit. In contrast, when goals are represented as feared states, we expected stronger associations between daily negative affect (particularly fear) and goal pursuit. We used seven days of repeated daily life assessments from 238 older individuals (Age: M = 70.5 years, SD = 5.99, 59-87 years; N = 119 couples). At baseline, participants reported three goals they planned to pursue over the study period and the extent to which each goal referred to something they hoped-for or feared. During the daily life assessments, participants reported their current affective states and momentary goal pursuit (goal engagement and goal progress) three times per day (11 AM, 4 PM, 9 PM). Multilevel analyses regarding participants’ most salient goal provide initial evidence supporting the expected interactions of goal representations on everyday affect–goal pursuit links. Specifically, individuals with a strong hope-focus in their goals engaged in more goal pursuit when positive affect was up than individuals whose goals were low in hope-focus. In contrast, those with feared goals engaged in more goal pursuit when reporting increased fear. Findings are discussed in the context of the possible selves literature. Future analyses will examine lead-lag effects to address the temporal order underlying affect-goal pursuit associations.
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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".