Fifty years of the British journal of learning disabilities: The power of the past
Bibliographic record
Abstract
Accessible summary This special issue of the journal celebrates the 50th birthday of the British Journal of Learning Disabilities (BJLD). The stories in this special issue are about the history of learning disability from around the world. It is important that people know about the history of learning disability, because people with learning disabilities have been kept in the dark for too long. Ian Davies says “why should we be forgotten? We're as much a part of society as everyone else”. Over the years BJLD has included stories about learning disability history, but many of these stories were written by people who do not have learning disabilities. In the past few years, people with learning disabilities have been doing important history projects. In this special issue, we have included stories about history that have been written by people with learning disabilities. In the next 50 years, it needs to be easier for people with learning disabilities to write for journals like BJLD. It should also be easier for people with learning disabilities to find out about other people's research in journals like BJLD.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.076 | 0.023 |
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".