BECOMING AN ETHNOGRAPHER IN A DIGITAL WORLD: ASSEMBLAGES, ETHICS & RHIZOMATIC PRACTICES
Bibliographic record
Abstract
As digital technologies and algorithmic systems reshape everyday life, ethnographic methods must adapt to new, fluid terrains.This paper explores the ethical and methodological challenges of conducting ethnography in digitally mediated environments, where boundaries between the real and virtual, human and non-human, are increasingly blurred.Drawing on Deleuze and Guattari's concepts of rhizome, becoming, and assemblage, the paper proposes a reconceptualization of ethnographic practice as non-linear, relational, and responsive to complexity.These ideas offer a framework for engaging with the shifting identities, structures, and ethical concerns of the digital age.By embracing rhizomatic thinking, this work invites ethnographers to reimagine the field as a dynamic assemblage of connections.It offers new pathways for reflexive, ethical, and theoretically grounded inquiry in networked spaces.
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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 | 0.021 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.037 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".