Bibliographic record
Abstract
Arguments and adjuncts play a crucial role in linguistic theories.Despite the vast body of research that assumes a distinction between arguments and adjuncts, not only in linguistics, but also in philosophy of language, psycholinguistics and neurolinguistics, there are no universally agreed-upon definitions distinguishing the two.The modest aim of this thesis is to investigate English speakers intuitions with respect to verbs and their arguments.To do so, the study makes use of the Core Participants Test, disguised in four different tasks, with each task eliciting, arguably, the same kind of intuitions.The results indicate that different tasks tap into either semantic or syntactic intuitions, or sometimes both.Overall, speakers' intuitions often matched linguists' views.to the members of my thesis committee, Ida Toivonen, John Logan, and Raj Singh.Ida Toivonen has taught and inspired me from the first time I heard her give a talk, despite the fact that at the time I understood only every other word.Through her knowledge, passion, patience and generosity she soon became my mentor and rolemodel.Ida taught me everything I know about arguments and adjuncts, syntactic theories, and ironically, along with Dana Isac, she taught me quite a bit about my native language.John Logan has taught me to think in an interdisciplinary fashion, and to translate my research questions and ideas across disciplines.Raj Singh's comments led to great improvements to the design of the study, and often made me aware of issues/alternative interpretations that I hadn't initially considered and which needed to be clarified.Each committee member had a great influence not only on this work, but also on shaping my Master's experience into a positive, productive, and enjoyable one.Aside from my committee members, whose help and guidance was constant and unconditional, I have been lucky enough to benefit from the knowledge of Masako Hirotani and Janna Fox.Masako Hirotani introduced me to EEG and fMRI data, and also to a series of behaviour questionnaires, such as the Autism Quotient test which was employed in this study.Most importantly, she taught me a great deal about work ethics, particularly about dealing with human participants and neurological data.Janna Fox has made me aware of issues with standardized tests and taught me how to create effective test items.A special thanks goes out to Ehsan Amjadian, Omid Beiraghi, Chris Genovesi, Krista Elliot, and Marly Mageau.Friends and colleagues, this great bunch of people were there along the entire journey, be it in person or through Skype, celebrating successes or editing papers, marking, or simply offering a hug at the right time.Thank you for making me my Master's experiences such a wonderful time
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".