Scientific Summaries for Families with ASD
Bibliographic record
Abstract
In the era of globalization and with the emergence of autism spectrum disorder as a global concern, the landscape of autism research has expanded to encompass much of the world. Here, we seek to provide an overview of the world of autism research, by documenting collaboration underlying the International Meeting for Autism Research (IMFAR), the pre-eminent annual scientific meeting devoted to the presentation of the latest autism research. We analyzed published abstracts presented at IMFAR meetings, between 2008 and 2013, to determine patterns of collaboration. We described collaboration networks on the individual, institutional, and international levels, and visually depicted these results on spatial network maps. Consistent with findings from other scientific disciplines, we found that collaboration is correlated with research productivity. Collaborative hotspots of autism research throughout the years were clustered on the East and West coasts of the U.S., Canada, and northern Europe. In years when conferences were held outside of North America, the proportion of abstracts from Europe and Asia increased. While IMFAR has traditionally been dominated by a large North American presence, greater global representation may be attained by shifting meeting locations to other regions of the world.
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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.259 | 0.057 |
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".