Finding the “Field” in our “Homes” and our “Homes” in the “Field”: A Critique of the “Home–Field” Dichotomy
Bibliographic record
Abstract
ABSTRACT The “home–field” dichotomy has long been a core assumption of fieldwork in political science. As in other social science disciplines, political scientists rely on these categories to contextualize our research within particular time–space nexuses and to separate our personal lives and private dwellings and institutions from our sources, participants, and broader research environments. Although the spatial, temporal, and emotional divisions between our “homes” and “fields” have always been arbitrary, they are increasingly blurred when we use remote and online methods for research, especially for qualitative studies. This article problematizes the home–field dichotomy within the context of remote and online political science field research. We contend that the overlap of our homes and fields in digital fieldwork poses different challenges for our professional boundaries than offline research, particularly in terms of separating our personal and research lives, mitigating risk, and protecting our mental health. Given the growing use of remote and online methods, we argue that the discipline of political science must account more seriously for the muddling of our homes and fields to support rigorous, transparent, and ethical empirical research.
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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 | 0.073 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.217 |
| Scholarly communication | 0.016 | 0.028 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".