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
<h3>Introduction</h3><br> The <a href="../../../Catalog/docs/LDC96S57/index.html" rel="nofollow">CALLFRIEND</a> project supports the development of language identification technology. <br> <h3>Data</h3><br> The corpus consists of 60 unscripted telephone conversations, lasting between 5-30 minutes. The corpus also includes documentation describing speaker information (sex, age, education, callee telephone number) and call information (channel quality, number of speakers). <br> For each conversation, both the caller and callee are native speakers of Spanish from Caribbean countries. All calls are domestic and were placed inside the continental United States, Canada, Puerto Rico, or the Dominican Republic. <br> Conversations were labeled as either "Caribbean" or "non-Caribbean" based on particular attributes in the speech of the participants. Callers in the "Caribbean" and "non-Caribbean" collections of CALLFRIEND Spanish were identified primarily on the basis of consonant quality patterns, specifically, word-final "s." <br> <h3>Updates</h3><br> As of June 7th, 2017 the file 'sp_4379.sph' was replaced to fix an anomoly. All copies downloaded after this date will have the full data. </br>
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it