General and Specific Issues for Researchers’ Consideration in Applying the Risk and Resilience Framework to the Social Domain of Learning Disabilities
Bibliographic record
Abstract
In this article, I discuss several general and specific issues that pertain to the risk and resilience framework. I propose that these issues deserve consideration by researchers using or interested in using the risk and resilience framework to guide their research in the social domain of learning disabilities. General issues discussed include: (1) integrating current research findings with those from prior longitudinal research by Emmy Werner and her associates, and from research in the 1980s and 1990s on problems in social perception and communication in children with learning disabilities; (2) measurement problems; and (3) the need for more differentiation in research regarding gender and the severity of learning disabilities. The specific issues discussed include: the need to continue to search for potential risk and protective factors; the need to research mediating processes or mechanisms that render a factor a risk or a protection; and the nature of intervention research.
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 | 0.222 | 0.277 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.064 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.008 | 0.019 |
| Research integrity | 0.018 | 0.021 |
| 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".