Undergraduate Academic Probation Policies in U.S. Universities: A Policy Discourse Analysis
Bibliographic record
Abstract
This paper explores undergraduate academic probation policies in United States universities from a policy discourse analysis perspective. Academic probation policies from 32 institutions were characterized by normalization and regulation. These encompassed classification, exclusion, and sanctions of students in order to produce retention. Consideration needs to be given to existing academic probation policies as discourse, their origins, the social identities, and particular realities that are being constructed by them. Institutions should question if academic probation policies are truly aligned with student retention to avoid these policies being an obstacle itself to student retention. Keywords: academic probation policy, higher education, critical discourse analysis, student retention. Cet article explore les politiques de probation académique des étudiants de premier cycle dans les universités américaines dans une perspective d'analyse du discours politique. Les politiques de probation académique de 32 institutions sont caractérisées par la normalisation et la régulation. Elles englobent la classification, l'exclusion et les sanctions des étudiants afin de favoriser la rétention. Il est nécessaire de prendre en considération les politiques de probation académique existantes en tant que discours, leurs origines, ainsi que les identités sociales et les réalités particulières qu'elles construisent. Les institutions devraient se demander si les politiques de probation académique sont réellement alignées sur la rétention des étudiants afin d'éviter que ces politiques ne soient elles-mêmes un obstacle à la rétention des étudiants. Mots clés : politique de probation académique, enseignement supérieur, analyse du discours critique, persévérance scolaire
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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 | 0.027 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".