Bibliographic record
Abstract
The ability to produce and understand referring expressions is basic to human language use and human cognition. Reference comprises the ability to think of and represent objects (both real and imagined/fictional), to indicate to others which of these objects we are talking about, and to determine what others are talking about when they use a nominal expression. The articles in this volume are concerned with some of the central themes and challenges in research on reference within the cognitive sciences - philosophy (including philosophy of language and mind, logic, and formal semantics), theoretical and computational linguistics, and cognitive psychology. The papers address four basic questions: What is reference? What is the appropriate analysis of different referring forms, such as pronouns vs. full noun phrases, demonstrative vs. personal pronouns, and overt vs. null/zero pronominal forms? Some of the papers assume and build on existing theories, such as Centering Theory and the Givenness Hierarchy framework; others propose their own models of reference understanding or production. The essays examine reference from a number of disciplinary and interdisciplinary perspectives, informed by different research traditions and employing different methodologies. While the contributors to the volume were primarily trained in one of four represented disciplines - computer science, linguistics, philosophy and psychology, and use methodologies typical of that discipline, each of them bridges more than one discipline in their methodology and/or their approach.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.256 | 0.144 |
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".