Narrative Online and Offline spaces. Field Notes from the Becoming of an Anthropologist
Bibliographic record
Abstract
To look back and reflect upon past field diaries, field encounters and events is the invitation of this paper, constructed both as a research note and as a personal research story. The invitation isaddressed especially to young anthropologists. The paper recalls and re-analyses data from three past online fields – one interactive website calling itself the “Romanian online community in Vancouver”, one online forum entitled the “Indian online community in Germany”, and the real-time communication portal Yahoo Messenger. It highlights the out-of-the-ordinary events recorded on each field, which illustrate complex relationships between the online and offline worlds. Further interpreting the fields as what contemporary American anthropologist Timothy Simpson, following Richard Sennett, calls “narrative spaces”, I hope to reveal more of the social construction of these virtual spaces. The main hypothesis to be explored and proposed for further debate are 1) that interactive virtual spaces develop as narrative spaces, around the frame-story offered by theirinitiators and 2) that narratives are continuously transcending different online and offline spaces, connecting them, while being continuously re-negotiated and re-told.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".