Ciało i naród. O doświadczaniu uczestnictwa i pozycjonowaniu antropologa w terenie
Bibliographic record
Abstract
This article offers reflections arising from my three-year field research with the Ukrainian diaspora in two Canadian cities. Drawing on this field experience, I present the body as a research tool and the impact of work performed by the ethnographer’s body. I discuss my multi-sensory field experience and the experience of participation, which are inter- twined with the increasingly important issue of ethnographer’s positionality in the field, and – in my view – the utopian freedom to choose or negotiate professional identities. My considerations are embedded within the insider-outsider dialectics (not opposition) and point to the contextual “nativeness” and “strangeness” of the researcher. I claim that the act of attribution of social class and ethnicity by our field partners influences our field- work and may have long-lasting consequences in the ethnographer’s later life, including their private life. I also discuss the fluidity and contextuality of a researcher’s familiarity with their field, including research situations where fieldwork is done with “one’s own people” or in cooperation with “one’s own people”, i.e. when and how familiarity is trans- formed into strangeness.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".