Bibliographic record
Abstract
07–165Crinion, J., R. Turner, A. Grogan, T. Hanakawa, U. Noppeney, J. T. Devlin, T. Aso, S. Urayama, H. Fukuyama, K. Stockton, K. Usui, D. W. Green & C. J. Price (U College, London, UK; c.price@fil.ion.ucl.ac.uk ), Language control in the bilingual brain. Science (American Association for the Advancement of Science) 312.5779 (2006), 1537–1540. 07–166Desai, Rutvik (U Trier, Germany), Lisa L. Conant, Eric Waldron & Jeffrey R. Binder, fMRI of past tense processing: The effects of phonological complexity and task difficulty. Journal of Cognitive Neuroscience (MIT Press) 18.2 (2006), 278–297. 07–167Kerkhofs, Roel (Radboud U, the Netherlands; roel.kerkhofs@mpi.nl ), Ton Dijkstra, Dorothee J. Chwilla & Ellen R.A. de Bruijn, Testing a model for bilingual semantic priming with interlingual homographs: RT and N400 effects. Brain Research (Elsevier) 1068. 1 (2006), 170–183. 07–168Kyung Hwan, Kim & Kim Ja Hyun (U Yonsei, South Korea), Comparison of spatiotemporal cortical activation pattern during visual perception of Korean, English, Chinese words: An event-related potential study. Neuroscience Letters (Elsevier) 394.3 (2006), 227–232. 07–169Paradis, Michel (McGill U, Canada; michel.paradis@mcgill.ca ), More belles infidels – or why do so many bilingual studies speak with forked tongue?Journal of Neurolinguistics (Elsevier) 19. 3 (2006), 195–208. 07–170Poldrack, Russell, A. (U California, Los Angeles, USA; poldrack@ucla.edu ), Can cognitive processes be inferred from neuroimaging data? Trends in Cognitive Science (Elsevier) 10.2 (2006), 59–63. 07–171Ylinen, Sari (U Helsinki, Finland; sari.ylinen@helsinbki.fin ), Anna Shestakova, Minna Huotilainen, Paavo Alku & Risto Näätänen, Mismatch negativity (MMN) elicited by changes in phoneme length: A cross-linguistic study. Brain Research (Elsevier) 1072.1 (2006), 175–185. 07–172Yokoyama Satoru (U Tohoku, Japan),Hideyuki Okamoto, Tadao Miyamoto, Kei Yoshimoto, Jungho Kim, Kazuki Iwata, Hyeonjeong Jeong, Shinya Uchida, Naho Ikuta, Yuko Sassa, Wataru Nakamura, Kaoru Horie, Shigeru Sato & Ryuta Kawashima, Cortical activation in the processing of passive sentences in L1 and L2: An fMRI study. NeuroImage (Elsevier) 30. 2 (2006), 570–579.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.267 | 0.197 |
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".