Task value profiles across subjects and aspirations to physical and IT-related sciences in the United States and Finland.
Bibliographic record
Abstract
Two independent studies were conducted to extend previous research by examining the associations between task value priority patterns across school subjects and aspirations toward the physical and information technology- (IT-) related sciences. Study 1 measured task values of a sample of 10th graders in the United States (N = 249) across (a) physics and chemistry, (b) math, and (c) English. Study 2 measured task values of a sample of students in the second year of high school in Finland (N = 351) across (a) math and science, (b) Finnish, and (c) the arts and physical education. In both studies, students were classified into groups according to how they ranked math and science in relation to the other subjects. Regression analyses indicated that task value group membership significantly predicted subsequent aspirations toward physical and IT-related sciences measured 1-2 years later. The task value groups who placed the highest priority on math and science were significantly more likely to aspire to physical and IT-related sciences than were the other groups. These findings provide support for the theoretical assumption regarding the predictive role of intraindividual hierarchical patterns of task values for subsequent preferences and choices suggested by the Eccles [Parsons] (1983) expectancy-value model.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".