Contradictions as Drivers for Improving Inclusion in Teaching Pupils with Special Educational Needs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The aim of this paper is to enhance understanding of the contradictions that arise in the drive to improve teaching practices among pupils with special educational needs (SENs). A questionnaire was administrated to 167 classroom teachers, subject teachers, special education teachers and teaching assistants in Finland. The analysis, based on thematic coding and analysis of manifestations of contradictions, revealed contradictions related to artefacts of teaching, participation in the community and school staff’s professional ability to teach SEN pupils in mainstream. Four types of manifestations, namely conflicts, critical conflicts, dilemmas, and double binds, could be identified in the data. Focusing the improvement of inclusive practices on all pupils calls for the collective resolution of contradictions and the development of tools and models that facilitate the educational change.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it