Bibliographic record
Abstract
In this article the author presents inclusive education as a policy, which in recent decades has often been viewed as a moral imperative. She presents this issue in a dichotomous choice: either full inclusion or special education. This chapter joins other voices in arguing for a more nuanced approach to inclusive practice. One that demands better quality research on which to base decisions and puts student needs and goals above blanket policies that are applied indiscriminately. The author oncludes future research also needs to remediate current limitations of research of the efficacy and effectiveness of inclusive education including: limited comparison or control groups, weak research designs, no measures of treatment fidelity, lack of clarity in the outcome measures (academic, behavioral, adaptive, etc.), limited attention to what the benefits and detriments of inclusion are, variable impact across populations, and to implement more randomized control trials.
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.
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 | 0.061 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.036 |
| Scholarly communication | 0.015 | 0.044 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.020 | 0.028 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".