Multiple goals: A review and derivation of general principles
Bibliographic record
Abstract
Summary A great deal of literature has examined the factors involved in single‐goal pursuit. However, there is a burgeoning realization that employees hold multiple goals at any one point in time and that findings from the single‐goal literature do not necessarily apply to multiple‐goal situations. Research is now being conducted on multiple goals, but it is being conducted across a broad range of disciplines, examining different levels of the goal hierarchy. Consequently, researchers are using the same label to refer to distinct concepts (the “jangle” fallacy) or different labels to refer to similar concepts (the “jingle” fallacy), and some aspects of the multiple‐goal space are yet to be examined. We derive seven general principles of the multiple‐goal process from a broad review of the literature. In doing so, we provide a common architecture and an overarching perspective of the theory for ongoing research as well as highlighting a number of areas for future research. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".