Bibliographic record
Abstract
Abstract This paper introduces a freely available and easy to use Java application for the collection and recording of Cloze probability ratings. Clozapp presents participants with text fragments of the researchers’ choice and collects guesses regarding upcoming words. It can also collect basic demographic information about participants. Available modes of data collection include elicitation of responses to a limited number of omitted words in a text or to all words in a text. Clozeapp can be customized to present instructions and experimental stimuli in any given language and to collect multiple types of demographic data. This paper presents the application by detailing the states and actions available, as well as descriptions of how to customize the app to fit different experimental needs including possible input and output details. The application manual is provided. As a proof of concept, we used Clozapp to conduct a replication study of two existing collections of Cloze probability norms. The Clozapp norms showed strong reliable correlations (r > 0.7) with both existing data sets, suggesting a high convergence between modes of data collection. The application provides an efficient and customizable way of collecting predictability norms for language research.
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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.121 | 0.063 |
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".