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
Abstract The maze task ( Forster, Guererra & Elliot, 2009 ; Forster, 2010 ) is designed to measure focal lexical and sentence processing effects in a highly controlled manner. We discuss how this task can be modified and extended to provide a unique opportunity for the investigation of lexical effects in sentence context. We present results that demonstrate how the maze task can be used to examine both facilitation and inhibition effects. Most importantly, it can do this while leaving the target sentence unchanged across conditions. This is an advantage that is not available with other paradigms. We also present new versions of the maze task that allow for the isolation of specific lexical effects and that enhance the measurement of lexical recognition through visual animation. Finally, we discuss how the maze task brings to the foreground the extent to which complex multi-layered priming and inhibition are intrinsic to sentence reading and how the maze task can tap this complexity.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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