Assessing the quality of online resources for inclusive research methods: Insights from a 2024 rapid review
Bibliographic record
Abstract
New academic researchers need resources to effectively learn how to conduct inclusive research and meaningfully engage co-researchers with intellectual and developmental disabilities. This rapid review aims to address: What online resources are available in 2024 for new academic researchers seeking to learn about inclusive research methods and what is the quality of those resources based on a set of criteria for assessing quality in inclusive research? A search of the literature was conducted. In total, 11 resources were included. A checklist with 11 items was developed to assess the quality of these resources. Results suggest a range of checklist criteria met among the resources. Among the 11 checklist items, some were consistently met while others were frequently overlooked. This study brings to light a need for addressing how senior academic researchers make resources for complex research methods available to others. Additionally, it demonstrates the importance of developing comprehensive, self-paced training with implementation support.
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How this classification was reachedexpand
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 | 0.021 | 0.188 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".