The development of case studies as a method within a longitudinal study of special educational needs provision in the <scp>R</scp> epublic of <scp>I</scp> reland
Bibliographic record
Abstract
When developing case studies within a longitudinal study of special educational needs provision within the Republic of Ireland, the authors were conscious of the critiques of the use of this approach within educational research. The difficulties associated with generalisation, challenges of ensuring trustworthiness and the possibilities of researcher bias have been identified as limiting factors in the presentation of case study data. In order to confront these limitations, the researchers developed a framework for case study development that aimed to provide a secure database and trustworthy interpretation in order to make assertions in relation to special educational needs provision. This paper describes this process and suggests that the need to develop safeguards in order to present case studies that have high degree of credibility is essential when using this approach. Furthermore, the transparency of research methods, a significant omission in many reports of research, is necessary in order to demonstrate the trustworthiness of data.
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 | 0.364 | 0.335 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".