The "Quilt" of Rural Education: Sewing Seams \nin Rural Special Education
Bibliographic record
Abstract
In rural and isolated communities, Learning Assistance / Resource Teachers (LARTs) are the front-line advocates for students with exceptionalities and their families. Thus, it is imperative that LARTs are provided with the necessary training and access to resources needed to meet the needs of the various students on their case files. In this thesis, I set out to explore what the specific needs of LARTs in rural or isolated areas are and how they can best be supported. By using action research and autoethnography, I place my personal experiences as an LART under the lens. I use the processes involved in quilting to illustrate what may happen when appropriate supports are not in place. The old adage "many hands make light work" holds true -- through access to time, specialized training, mentorship, access to resources, and time to collaborate, rural LARTs have the power to make a positive impact on the life of a child. My hope is that what I have learned will then become a “pattern” to be used and refined by other LARTs as a future catalyst for change in the area of rural/isolated Special Education.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".