Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Concepts and word meaning are fundamental to nearly all aspects of human cognition. People use this knowledge daily to recognize entities and objects in their environment, generate expectancies for upcoming events, and interpret language. In this chapter, we review contemporary research in semantic memory. Our discussion is restricted to the meaning of individual words, focusing on recent experimental results and theoretical trends. Over the past number of years, semantic memory research has blossomed for a number of reasons, and our goal is to provide the reader with a feel for the exciting research and theoretical approaches that have resulted. The chapter deals primarily with the following topics: implications of grounded cognition for semantic memory, neural organization of concepts, the importance of people’s knowledge of everyday events for semantic memory, distinctions among semantic and associative relations, research on abstract concepts, connectionist models of semantic computations, and distributional models of semantic representations.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it