Mapping Future Research in Disabilities—Research Initiatives in Intellectual Disabilities in India: Report of a National Interdisciplinary Meeting
Bibliographic record
Abstract
Abstract A meeting organized under the auspices of the International Association for the Scientific Study of Intellectual Disabilities (IASSID) Academy on Education, Teaching and Research was held in March 2011 at the India International Centre in New Delhi, India, with the explicit purpose of helping establish a road map for future research in intellectual disabilities (ID) in India and to forge alliances among like‐minded researchers and practitioners to move forward on countrywide research. The participants were drawn from nongovernmental and governmental groups, private consultancies, psychology, researchers, and policymakers, and they were interested in research dissemination, research methodologies, and research ethics. The participants developed a framework for research strategies and defined important areas for further research in ID for India. Based on the discussions, the following research strategy areas were identified: prevalence studies, human ethics, human rights, prevention, interventions, research syntheses of existing research that has been conducted in India, dissemination of existing research, building research capacity, and disaster preparedness. As each priority area required further discussion and feedback, agreement was achieved that such further discussions and collaborations would follow. The follow‐up is a process which involves the simultaneous and synergistic development of policies and practices at the application and governmental levels.
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 | 0.037 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".