Beyond the excused/unexcused absence binary: classifying absenteeism through a voluntary/involuntary absence framework
Bibliographic record
Abstract
Student absenteeism in secondary schools has received international academic attention for quite some time. Absenteeism has been linked to diminished academic outcomes and is one of the leading causes of high school dropout. Although absenteeism is a serious concern for educational scholars, the definitions of absences and their subtypes are inadequately developed in academic literature. The overreliance on excused/unexcused absences that posit the school and the family as the arbitrators of the validity of an absence is a troubling concern, as it glosses over the underlying causes for an absence. This study outlines and critiques the varying conceptions of absenteeism found in the literature and proposes the use of the voluntary/involuntary absenteeism framework as a viable approach to studying non-attendance. The voluntary absence concept is cognizant of the motivational factors affecting student attendance. When the school is perceived as a hostile environment that is often equated with failure some pupils may voluntarily choose to avoid the institution. Involuntary absences refer to absences that are imposed on the student by the conditions of her or his life. Having to work to supplement familial income can often negate a youth’s ability to be present in school. This framework provides an opportunity to both acknowledge the students’ agency in deciding when to attend and to investigate deeper the students’ life circumstances that hinder regular attendance.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".