Q2Stress: A database for multiple cues to stress assignment in Italian
Bibliographic record
Abstract
In languages where the position of lexical stress within a word is not predictable from print, readers rely on distributional information extracted from the lexicon in order to assign stress. Lexical databases are thus especially important for researchers willing to address stress assignment in those languages. Here we present Q2Stress, a new database aimed to fill the lack of such a resource for Italian. Q2Stress includes multiple cues readers may use in assigning stress, such as type and token frequency of stress patterns as well as their distribution with respect to number of syllables, grammatical category, word beginnings, word endings, and consonant-vowel structures. Furthermore, for the first time, data for both adults and children are available. Q2Stress may help researchers to answer empirical as well as theoretical questions about stress assignment and stress-related issues, and more in general, to explore the orthography-to-phonology relation in reading. Q2Stress is designed as a user-friendly resource, as it comes with scripts allowing researchers to explore and select their own stimuli according to several criteria as well as summary tables for overall data analysis.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.010 |
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".