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 lieu of an abstract, here is a brief excerpt:Since the last attempts to establish sub-grouping in Athapaskan (cf. Mithun 1999), the field has benefited from a wider availability of data through published grammars, dictionaries, and articles, as well the greater ease of accessibility to digital archives containing field notes and other relevant primary materials. Additionally, computer-aided techniques of data analysis have been developed, making it possible to treat larger sets of data and more readily visualize these data with graphs and maps. Here, we present the results of applying statistical clustering and mapping techniques in grouping Athapaskan languages on the basis of phonological similarity. We want to argue for the usefulness of applying such techniques to Athapaskan, and point toward future work that will integrate greater and more varied bodies of data that we believe will lead to a reliable sub-grouping of Athapaskan languages and bring greater understanding of the history of the Athapaskan-speaking peoples.
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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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