Bibliographic record
Abstract
In the past decade, as attention to language documentation has increased, so too has discussion of the goals of linguistic documentation with respect to the community of speakers, and community-based research paradigms have come increasingly to the fore. What happens if it is not possible to carry out linguistic work in a location where a language is traditionally spoken? Is it possible to create a kind of community-based project in such a situation? In 2006 in a linguistics field methods class, the speaker turned to the class and remarked that there was no grammar of Esan (Niger-Congo, Nigeria), the language under study, and very little in the way of written materials was available on the language. She challenged the class, a mixed group of advanced undergraduates and graduate students, to take on the writing of a grammar. A group of students decided to take up this challenge, and this was the beginning of the Esan Grammar Project. Most of one term was spent coming to an understanding of what a grammar is, and what the responsibilities to the Esan community, living an ocean away, were. Students formed groups, depending on their interests, and took on the responsibility for different aspects of the grammar. The speaker sought out other speakers of the language in the local community so that the grammar would represent more than the speech of a single individual, and it would be possible to look at language in use to some degree. A community arose through the students and speakers working together to build a grammar of the language, with each contributing to the knowledge of the others. This local community formed the backbone of the project in the absence of the Esan community in Nigeria, facilitating the documentation of the language as well as enhancing the pride that the speakers involved in the project take in the language. The goal of the project is to create a product which can be used both within the field of linguistics, and by the larger Esan community we have become intimate with. In this presentation, we outline some successful strategies we as a group have employed to overcome setbacks and challenges, and discuss the next phases of our project: sending a student and one of the speakers to conduct fieldwork in Nigeria, and have a grammar ready for publication by the end of 2009.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.076 | 0.014 |
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".