Bibliographic record
Abstract
Ethnography has traditionally involved the sustained presence of an anthropologist in a physically fixed field setting, intensively engaged with the everyday life of the inhabitants of a given site, typically, a village or other small community. Conventional notions of the field, especially in anthropology which has been the premiere field-based discipline (see Amit, 2000; Gupta & Ferguson, 1997, 1992), involved basic assumptions of boundedness (the field was a strictly delimited physical place); distance (the field was “away,” and often very far away as well); temporality (one entered the field, stayed for a time, and then left); and otherness (a strict categorical and relational distinction between the outsider/ethnographer and the insider/native informant). The key mode of ethnographic engagement in the field was, and is, that of participant observation. When the Internet enters into ethnography, and when ethnography acquires an online dimension either in the research process or in the production of the documentary outputs of research, we end up facing a situation that leads us to reconsider relationships between the researchers and those who are researched. This is especially true of collaborative, action research projects that involve researchers and activists producing materials for the Web.
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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".