What can emotion and abstract words tell us about context availability ratings?
Bibliographic record
Abstract
Abstract Semantic dimensions such as context availability, imageability and valence, form core components of many theoretical accounts of lexical processing. Typically, normative data for such semantic dimensions are drawn from subjective ratings, however, questions have been raised regarding the reliability and validity of these ratings. In this paper, we discuss this issue with a focus on context availability norms. Using data collected for another study, we show that context availability ratings required significantly higher rates of data exclusions at the level of both participants and items compared to other variables. In addition, high standard deviations at the item level, indicated a substantial degree of disagreement between participants. This suggested that some participants may have had difficulty understanding the concept of context availability which hindered their ability to complete the ratings. We provide recommendations for future research focusing on context availability and for norm collection procedures more broadly in order that the validity of such norms can be improved. In particular, we suggest that clear guidelines are required for data cleaning in order that the reliability of such norms is maximised and to facilitate replication across studies.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".