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
In the field of computational linguistics, spoken language recognition (through the use of wordlists and morphological markers) is a resource-intensive process: the input must be parsed from the inputted speech signal, words must be hypothesized, and then subsequently word-lists for any likely language must be iterated through. To note, spoken language recognition does not refer to the process of identifying the meaning of the input; rather, it is finding the language of which the speaker is speaking (not necessarily 'parsing' the input). In my research, the question of whether a language can be positively and uniquely identified through small nuances found in the individual formants of vowels is examined. Through analysis of language samples from the Heritage Language Variation and Change (HLVC) corpus (courtesy of Dr. N. Nagy (University of Toronto), pan-linguistic formant frequency distribution was examined. Tabulation of the first three formant frequencies was performed, and through analysis of formant distribution histograms, it is clear that all of the languages in question (Italian, Korean, and Ukrainian) show enough variation to be positively identified.
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 | 0.000 |
| Science and technology studies | 0.001 | 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 it