Bibliographic record
Abstract
In order to understand better the relationship between personal and environmental variables as determinants of performance, the present study investigated relevant literature in the behavioral sciences on motivation, emotion, ability and performance. Maier's performance formula and Vroom's motivation equation were analyzed and re-interpreted, using the concepts of vector and scalar quantities and taking into consideration human limitations. It was demonstrated theoretically that Maier's performance formula does not account for the possibility that performance could decrease when a subject is highly motivated, although Young, McClelland and others have found that this is possible empirically. Emotion was postulated to be the cause of this phenomenon. Based on the theory of emotion as advanced by Leeper, Duffy and Young, and the theory of activation as formulated by Malmo, Hebb, Schlosberg and Lindsley, emotion was postulated as a possible moderator influencing the relationship between motivation and performance. Behavioral efficiency in work performance was assumed to be an inverted U-shaped function of emotion arousal. The motivation variable in the performance formula was based on the cognitive theory of motivation as postulated by Tolman and Lewis and subsequently modified by Vroom and Lawler and Porter. However, the concept of a reciprocating contractual relationship between performance and reward and the concept of a "multiple-discount" for the interactive relationship between valence and expectancy were incorporated into the cognitive theory of motivation. By using qualitative interactive tests and hypothetical values for the variables, the interactive relationship between expectancy and valence in determining motivation was found to be algebraic multiplicative and the interactive relationship among motivation components toward various incentive components were found to be vector additive. Further, the algebraic multiplicative operator was found to be most appropriate to describe the interaction among ability, motivation and behavioral efficiency as determinants of performance. It was concluded that the theoretical formula could be operationalized and that it could help managers to understand better the relationships between behavioral and economic variables so that scarce economic resources could be more efficiently utilized.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".