Bibliographic record
Abstract
What does one need to know, ideally, when beginning to do fieldwork? As I think back to when I first did fieldwork, review my years of active and intensive fieldwork, and consider teaching a field methods course, many different things come to mind. In this article, I will concentrate on a few lessons about the linguistic aspects of fieldwork that I learned early on in doing this work, including the following items, which can perhaps be thought of as slogans to keep in mind when preparing to do fieldwork: a) Pay careful attention to information about the language that the speaker you are working with wants you to hear. b) Know the available literature and respect it, but keep in mind that there is always more to learn. c) Avoid isolating areas of the language so that you lose track of the fact that language is a complex, dynamic system. d) Bring as much knowledge as you can, from all domains – about language, about linguistics, about people. e) Do not straightjacket the language into categories that you bring to it – let it live on its own. f) Do not think that language is a monolithic entity within a community. There is variation within language, and this must be part of any analysis. g) Not all speakers have the same strengths. h) A good working relationship is an evolving thing. Both speakers and the linguist must get to know one another. i) Be open to learn.
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.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.013 | 0.027 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.030 | 0.023 |
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".