Having a shinshii/shiishii ‘master’ around makes you speak Japanese!
Bibliographic record
Abstract
Everyone brings with them a particular set of contextualization to interaction. After several years of working on the Ikema dialect of Miyako Ryukyuan, we are finally realizing that our very presence as shinshii/shiishii ‘masters’ is one of the major factors encouraging Ikema people to use Japanese. Because of the way our social identity is perceived among the Ikema people, our work session turns out to be an inherently a Japanese speaking context, which not only makes collection of naturalistic discourse data rather precarious but also can influence even the native speakers’ grammatical intuition of Ikema. Our goal in this paper is to explicate the very sensitive nature of the fieldwork setting and its implication for data collection by illustrating how the identity of researchers themselves can inadvertently shape the context and in turn shape the choice and use of language in fieldwork situations.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".