(Re)thinking the Adoption of Inclusive Education Policy: A Conceptual Shift
Bibliographic record
Abstract
The purpose of this article is to advance a proposal for the analysis of the adoption of inclusive education policies. In particular, I use the notion of ‘Policy Enactment’ to re-conceptualize the process of putting inclusive education policies into practice. The argument is that the traditional ‘implementation’ model to analyze the adoption of inclusive education policies devalues the significant role of context in both policy analysis research and practice. Analyzing education policies based on the construct of ‘enactment’ offers a context-informed approach that can help policy actors and researchers to better understand how inclusive education policies are incorporated into practices. The paper concludes that a policy enactment perspective empowered by a Neo-Institutional lens, is a more robust tool for analyzing inclusive education policies as their translation is far from being a mere upfront conversion of text into action.
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 | 0.052 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.113 |
| Scholarly communication | 0.033 | 0.039 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.014 | 0.022 |
| 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".