10.1177/0013916502250753ARTICLEENVIRONMENT AND BEHAVIOR / July 2003Villacorta et al. / V LI ATION OF MTE SCALE FURTHER VALIDATION OF THE MOTIVATION TOWARD THE ENVIRONMENT SCALE
Bibliographic record
Abstract
primary areas of research interest are motivation, self-regulation, and goal orientation. RICHARD KOESTNER received his Ph.D. from the University of Rochester. He is currently an associate professor in psychology at McGill University. His primary ar-eas of research interest are self-regulation and goal-setting. NATASHA LEKES received her B.A. from McGill University and is currently study-ing community psychology at Harvard University’s Graduate School of Education. Her primary areas of research interest are the evaluation of programs and techniques to foster children’s motivation, social competence, and moral development. ABSTRACT: A study was conducted to further validate the Motivation Toward the Environment Scale (MTES). Results confirmed both the convergent and discriminant validity of the MTES by showing that peer reports corresponded to self-reports of environmental self-regulation and that environmental self-regulation was relatively distinct from self-regulation in academic and political domains. Results also pointed to some possible sources of autonomous self-regulation. Individuals were more likely
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.940 | 0.860 |
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".