Finding a Home: Inclusive Post-secondary Education and the Adult Education Field
Bibliographic record
Abstract
This article reports the results of a study that undertook to describe a new adult education phenomenon called Inclusive Post-Secondary Education (IPSE). From interviews with senior staff members of three IPSE programs in Alberta, Canada, an operational definition of common practices across IPSE programs was developed. Interviews also produced a listing of benefits and outcomes for students attending these programs. Results of this study suggest a common operational model that could be replicated elsewhere and reveal some philosophical premises that appear to be inherent in the operation of IPSE programs. Résumé Cet article rapporte les résultats d'une étude qui vise à décrire un nouveau phénomène lié à la formation permanente, soit l'éducation post-secondaire universelle (EPSU). A partir d'entrevues réalisées auprès de cadres supérieurs qui travaillent dans trois programmes de type EPSU en Alberta, au Canada, une définition opérationnelle des pratiques communes à ces programmes a été élaborée. Les entrevues ont également mené a l'établissement d'une liste d'avantages et d'aboutissements pour les étudiants qui fréquentent ces programmes. Les résultats de cette étude laissent apparaître un modèle opérationnel commun qui pourrait se transposer ailleurs et révèlent certains principes d'ordre philosophique qui semblent être inhérents à l'exploitation des programmes de type EPSU.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".