School Engagement Trajectories and Their Differential Predictive Relations to Dropout
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
- Candidate categories
- none
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: QualitativeConsensus signal: none
- Genre
- Candidate signal: EmpiricalConsensus signal: Empirical
- Teacher disagreement score
- 0.803
- Threshold uncertainty score
- 0.868
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.293 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Although most theories draw upon the construct of school engagement in their conceptualization of the dropout process, research addressing its hypothesized prospective relation with dropout remains scarce and does not account for the academic and social heterogeneity of students who leave school prematurely. This study explores the reality of different life‐course pathways of school engagement and their predictive relations to dropout. Using an accelerated longitudinal design, we used growth mixture modeling to generate seven distinct trajectories of school engagement with 12‐ to 16‐year‐old students (N = 13,300). A vast majority of students were classified into three stable trajectories, distinguishing themselves at moderate to very high levels of school engagement. We refer to these as developmentally normative pathways in light of their frequent occurrence and stability. Although regrouping only one‐tenth of participants, four other nonnormative (or unexpected pathways) accounted for the vast majority of dropouts. Dropout risk was closely linked with unstable pathways of school engagement. We conclude by debating the delicate investment balance between universal strategies and more selective and differentiated strategies to prevent dropout. We also discuss the need to better understand why, within normative trajectories, some students with high levels of school engagement drop out of school .
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.
The record
- Venue
- Journal of Social Issues
- Topic
- Early Childhood Education and Development
- Field
- Social Sciences
- Canadian institutions
- Université de Montréal
- Funders
- not available
- Keywords
- Dropout (neural networks)PsychologyConceptualizationNormativeDrop outDevelopmental psychologyConstruct (python library)Longitudinal studySocial psychologyMedicine
- Has abstract in OpenAlex
- yes