Predicting Trajectories of Vocational Indecision from Motivational Profiles in Early Adolescence
Bibliographic record
Abstract
Abstract Background and objective During emerging adulthood, vocational indecision (i.e., the inability to make coherent career choices) develops in a heterogeneous fashion, with three distinct patterns: low; decreasing (i.e., developmental or adaptative); high and stable or increasing (i.e., chronic, or maladaptive). Among the determinants of vocational indecision that have been identified in past research, academic motivation is an excellent choice, since it is at school that students' vocational choices are validated or not. So, the purpose of this longitudinal study was to verify if the patterns found with emerging adulthood students characterized vocational indecision in adolescent students, and if so, to predict the belonging to the most problematic trajectory by using students’ academic motivational profiles. Academic motivation was assessed using self-determination theory. Method and Results Using data from 384 students (56% female) surveyed annually from Secondary 2 to 5, person-centered analyses allowed to estimate motivational profile in Secondary 2 and vocational indecision trajectories during the 4-year period. Results revealed four distinct patterns of vocational indecision during adolescence labelled Low and Stable, Moderate and Stable, Developmental and Chronic Intermittent. Four motivational profiles were also identified in Secondary 2, ranging from poor (Highly Amotivated) to moderate (Autonomous-Introjected) quality of self-determination level. Also, in reference to the most self-determined profile, students in the Mixed profile were at greatest risk of following Chronically-Intermittently Undecided trajectory. Finally, the most self-determined students were at greatest probability of following the Developmentally Undecided trajectory. Conclusion Overall, the findings suggest that the student motivational functioning in early secondary school years could be used to identify students at risk of experiencing the negative indecision patterns across secondary school. Several theoretical and practical implications are suggested.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".