Bibliographic record
Abstract
The purpose of this thesis is to augment our empirical knowledge of the English resultative and to provide a theoretical treatment of the syntax and semantics of the resultative using current linguistic tools.This involves using both theoretical and experimental methods to ensure that the model reflects human usage of natural language and follows the principles required for formal language modelling.The main questions addressed are 1) Is the result phrase an argument, an adjunct, or something else (an added/derived argument, sometimes called an argumentadjunct)?2) How do we best capture the properties of the resultative in a formal model?In order to address these questions, it must be determined what falls into the overall category of the resultative and how the resultative can be sub-classified into further sub-categories to best reflect the potentially distinctive properties of related resultative constructions.These divisions are then tested for fit with the theoretical categories of argument, adjunct and added/derived argument.Lastly, an analysis is provided using Lexical-Functional Grammar and Glue Semantics.The work of writing a thesis is never a singular effort.Many people become important partners in the project over the time it takes to complete, and my journey was no different.Without the tireless efforts of those around me, the work that I did would have just been one person sitting at a desk typing, and would never have become a complete project in the end.To begin with, I would like to thank the scholarships and funding bodies which made my PhD possible, including the 2010 President's Doctoral Fellowship, the Kevin Sampson Scholarship, and the Wargaret Wade Labarge Fund.Without their generous support, this project would have never begun, let alone come to fruition in the form that it is now.Next, I would like to thank the administrative team within my department: Colleen Fulton, May Hyde, Liane Dubreiul, Georgina Henderson, and, more recently, John Tracey.Your helpful knowledge and friendly smiles have made being in the department for the last five years much easier and more enjoyable.To the members of the Logic, Language and Information Lab: thank you for all of your helpful comments and feedback, as well as the opportunity to share my ideas in a small group environment.To my committee members, Kumiko Murasugi and John Logan
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".