Moving Beyond Two Goals: An Integrative Review and Framework for the Study of Multiple Goals
Bibliographic record
Abstract
Historically, the study of multiple goals has focused on the dynamics between two goals as the prototypical example of multiple goals. This focus on dyadic relations means that many issues central to the psychology of more than two goals are still unexplored. We argue that a deeper understanding of multiple goal issues involves moving beyond two goals. Doing so not only reveals new insights about goal relations (e.g., how one dyadic relation affects another), but also introduces goal structure (how goals and goal relations are positioned relative to each other) as a variable in its own right worthy of study. In our review, we discuss current knowledge gaps, review methodologies both in terms of existing techniques and novel techniques we propose, and highlight new directions from moving beyond two goals—what new questions emerge and what dynamics, including intersectional issues (e.g., between goal properties and goal structure), become possible to explore.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".