Bibliographic record
Abstract
The theory of goal setting is generalizable across more than 100 different tasks in various occupations (Latham, 2009 ) and across numerous countries, including Australia, Canada, China, Europe, Israel, and the United States (Locke & Latham, 1990 ), suggesting that goal setting is one of the most valid and practical theories of motivation (Lee & Earley, 1992 ). Despite such robust fi ndings, fi eld and laboratory studies on goal setting have typically measured goal setting attributes of specifi city or clarity (the degree of quantitative precision with which the aim is specifi ed) and diffi culty (the degree of profi ciency or level of performance sought) in different ways (cf. Austin & Vancouver, 1996 ; Lee & Bobko, 1992 ) with psychometrically untested items, scales, or manipulation checks (Lee, Bobko, Earley, & Locke, 1991 ). One reason for this may be that the systematic development of goal setting measures has been rather limited. For example, the most complete measure of goal setting was proposed and developed by Locke and Latham ( 1984 ). The scale was designed to capture the core goal attributes of specifi city and diffi culty, as well as support elements such as supervisor support and worker participation, and providing rationales for the goals set and feedback on goal progress. Support elements ensure that the goals set will be channeled into successful actions (Lee et al., 1991 ). Goals, however, can be dysfunctional when achieving a goal is seen as a way to avoid negative outcomes, or to please one’s boss. Additionally, too many and too diffi cult goals can lead to elevated stress and confl ict (Latham & Locke, 2006 ).
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
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".