Bibliographic record
Abstract
Montrealis a truly cosmopolitan city with great street life and great food and entertainment.It is also a city that celebrates differencethe theme of our meeting.The collision of the English and French cultures and the mixing in of cultures from around the world makes for a virtual gold mine of opportunity for innovation and excitement.And that is what we want you to get out of this meeting.Our job is to examine differences, learn from others' experience and work to challenge each others assumptions.Our Scientific Program Committee has worked hard over the year to bring you the right mix of peer reviewed presentations and instructional courses and state-of-the art plenary sessions.Our meeting also celebrates difference through the range of professionals we bring together to understand better, describe and treat children who have developmental disorders.It is our strength and we hope that you will find in the program the opportunity to see the differences in the way our various disciplines contribute to solutions that are good for the children and youth we care about.The real opportunity to celebrate difference is what happens to you during this meeting.We want you to take in the Montreal environment, to mix it with good science and quality time with new and old friends, and come away with something differenta new idea for practice, an opportunity to collaborate on research, an educational gem.It is this opportunity to create and experience difference that leads us to improve.Celebrating difference is what it is all about!So, on behalf of all those who have worked to bring this meeting to you, welcome and enjoy.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.631 | 0.539 |
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".