Cognitive Profiles of Mexican Elementary Education Teachers on School Inclusion of Students with Disability
Bibliographic record
Abstract
This research investigated the attitudes of 99 elementary education teachers toward the inclusion of students with disabilities in schools. The present authors designed a factorial experiment with 2 (View on learning difficulties: static vs. interactionist) x 2 (Teacher approach: teaching vs. learning) x 2 (Social climate: homogeneous vs. heterogeneous) x 3 (Institutional culture: individualistic vs. delegation vs. collaborative). The orthogonal combination of these factors resulted in 24 experimental conditions ready to build the 24 experimental scenarios of this study. Each scenario described a hypothetical story about the thinking of teachers who faced a school inclusion situation. The experimental task for the participants was to read each scenario individually and then assess their level of identification with the protagonist. The results indicated two types of teacher profiles among the participants: the pre-inclusive profile, which showed a low level of identification towards the inclusive vision, and the inclusive profile, which presented a moderate level of school inclusiveness. The most relevant factors for making inclusive judgments in both groups were the diversity climate (hp2 = 0.24) and professional culture (hp2 = 0.13). However, the pre-inclusive group showed a greater affinity with school homogeneity and individual work. In contrast, the second group showed greater acceptance of diversity and collaboration in school inclusion. In this article, the present authors discuss the implications of these findings within the classroom.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".