Goals Across Time Frames and Temporal Landmarks: Do Time of Year and Goal Age Influence Goal Perceptions?
Bibliographic record
Abstract
Research on personal goals is conducted at various times during the year, and researchers ask participants about their goals in different ways, eliciting both new goals and goals that have been pursued for varying amounts of time. But do these differences affect how people perceive and report on their goals? In line with the fresh start effect, which proposes that people think differently about goals around temporal landmarks, we anticipated that people would perceive goals set and reported on around the New Year differently than goals set or reported on at other times of the year. Participants (N = 362) reported on three goals either in January or in March and rated them on 16 different characteristics. The goals were either young (set since January 1 st or in the last month) or older goals that participants were already pursuing. Results showed that participants rated their goals very similarly in January and March. There were, however, many differences in perceptions based on goal age, with greater avoidance, controlled motivation, difficulty, abstractness, and conflict for older goals (compared to newer goals). Other goal characteristics (autonomous motivation, commitment, importance, effort, self-efficacy, approach motivation, and goal facilitation) did not differ by goal age. Participant’s self-reported progress on their goals was also tracked monthly over 6 months. Progress generally followed a quadratic (inverse U) trajectory, initially increasing, then plateauing and slightly decreasing across the 6 months. There were, however, differences between goals set in January and those set in the last month, with January goals more closely following the quadratic pattern. Overall, this research contributes to our understanding of how goal perceptions evolve over time, highlighting the underexplored role of goal age and the seemingly limited role of temporal landmarks.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".