Semantic Ambiguity and the Process of Generating Meaning From Print.
Bibliographic record
Abstract
An ambiguity disadvantage (slower responses for ambiguous words, e.g., bank, than for unambiguous words) has been reported in semantic tasks (L. R. Gottlob, S. D. Goldinger, G. O. Stone, & G. C. Van Orden, 1999; Y. Hino, S. J. Lupker, & P. M. Pexman, 2002; C. D. Piercey & S. Joordens, 2000) and has been attributed to the meaning activation process. The authors tested an alternative explanation; The ambiguity disadvantage arises from the decision-making process in semantic tasks. The authors examined effects of ambiguity on unrelated trials in a relatedness decision task, because these trials are free from response competition created by ambiguous words on related trials. Results showed no ambiguity effect on unrelated trials (Experiments 2, 3c, and 5c) and an ambiguity disadvantage on related trials (Experiments 3a, 3b, 5a, and 5b).
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".