Bibliographic record
Abstract
There is a long history of thought and research in the social sciences that views human beings as engaged in entirely instrumental activities in pursuit of goals that typically give them pleasure. This view makes a sharp distinction between “means” and “ends,” and treats the relation between means and ends as essentially arbitrary. Forty years of research on “intrinsic motivation” presents a different view, suggesting that some activities are themselves ends. In this chapter, we argue that distinguishing between intrinsic and extrinsic motivation has been important, but that the current understanding of the distinction is not adequate to capture the most important dimensions of difference between these two types of motives. We suggest a modification of the distinction, between activities that are pursued for consequences that bear an intimate relation to the activities themselves, and those that are purely instrumental. We call the former class of activities “internally motivated,” and argue that while they are not necessarily pleasurable, they yield lasting effects on well-being that instrumental consequences typically do not. Further, we argue that internally motivated activities differ from intrinsically motivated ones, in which the sheer pleasure of the activity motivates its pursuit. We discuss evidence from both laboratory research and field studies, including a longitudinal study of West Point cadets, in support of our arguments.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".