The Case of the Disappearing/Appearing Slow Learner: An Interpretive Mystery
Bibliographic record
Abstract
This thesis explored the topic of the categorization of and programming for students named, through intellectual assessment and/or documented school failure, as “slow learners”. Written as a fictionalized hard-boiled detective story instead of adopting a more traditional thesis format, the thesis drew on the author’s experiential data, primary sources, and interviews with students, teachers, administrators, and curriculum leaders and the interpretive lenses of disability studies, including disability history, and hermeneutics. It explored assumptions contained in the slow learner label and the resourcing and accommodation practices, and their lack, that flow from this and other educational labels. Emergent themes included the harmful consequences of sorting individuals by measured intelligence scores, and the notion that the complexity of human learning for any student is greater than the slow learner label, or any educational label can contain. Paradoxically, even as these themes emerged, the actual teaching practices in many programs for slow learners, in their concreteness, in their freedom from constraints such as standardized testing, and in their use of inquiry methods, were reported as beneficial to these students and potentially to other students as well. When similar methods were used in non-segregated classrooms that included students named as slow learners, most students were reported to be engaged and successful. In this vein, broader educational reform measures that might be potentially helpful in helping make schools more inclusive for the students currently labelled slow learners were also examined. This thesis recommended the use of inclusive approaches in classrooms at the site-based level as well as continued scrutiny and reform of the institutional barriers at the school, district, and provincial levels that contribute to the production of slow learners.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.073 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".