Eyes on the prize: The preference to invest resources in goals over means.
Bibliographic record
Abstract
Goal systems are hierarchical, often requiring people to invest resources vertically-both in lower-order means and higher-order goals. For example, a college student who wants to take a particular class (a goal) might first have to take a prerequisite (the means). We investigated how the hierarchical configuration of goals and means affects preferences for vertical resource allocation. Specifically, we found that within goal-means dyads, people preferred to shift resources toward goals (i.e., invest less in means and more in goals) and further invested more resources in items framed as goals (versus means; Studies 1-2). The preference to shift resources toward goals was moderated by the presence of a goal-means hierarchy within the dyad (Study 3) and mediated by the perception that investing resources in the goal was a more direct investment in goal attainment (Study 4). Moreover, people chose to reduce costs associated with means (versus goals; Study 5) and were happier when costs associated with means (versus goals) were eliminated (Study 6). These studies demonstrate that the aversion to investing resources in means can result in non-normative decision making in the course of goal pursuit. (PsycINFO Database Record
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.007 | 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".