Dichotic listening with specific, general, abstract and emotional words : semantic judgments and reaction times
Bibliographic record
Abstract
Welcome to the Ninth International Conference on the Mental Lexicon.It has now been 16 years since our first conference in Edmonton in 1998.From the outset, our goal has been to provide a forum for the exchange of new findings and perspectives on how words are represented and processed in the mind and brain.We hope that you enjoy the historic setting of Niagara-on-the-Lake and the facilities of the Queen's Landing Hotel.Most importantly, we hope that you will have plenty of opportunity for collegial interaction.As has been our tradition, platform presentations are held in a single joint session and poster sessions constitute the backbone of research dissemination at the conference.In this opening page, we would like to take the opportunity to thank the individuals and organizations that have made the Ninth International Conference on the Mental Lexicon possible.The conference is hosted by McMaster University and Brock University.We are grateful for the support that we have received from the Dean of Humanities at Mc-Master University as well as the Vice President Research.We are also grateful for the support of the Office of the President at Brock University as well as its Conference Services Department and support group.We would also like to express our gratitude for the hard work of the Scientific Committee.We have all benefitted greatly from the judgment, expertise and hard work of the international team consisting of Raymond Bertram (Finland), Doug Davidson (Spain), Mirjam
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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 | 0.001 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".