The <i>terroir</i> of bureaucratic practice: Everyday life and scholarly method in the study of policy
Bibliographic record
Abstract
This article seeks to connect political geographic scholarship on institutions and policy more firmly to the experience of everyday life. Empirically, I foreground the ambiguous and indeterminate character of institutional decision-making and I underscore the need to closely consider the sensory texture of place and milieu in our analyses of it. My examples come from the study of diplomatic practice in Brussels, the capital of the European Union. Conceptually and methodologically, I use these examples to accentuate lived experience as an essential part of research, especially in the seemingly dry bureaucratic settings. I do so in particular through engaging with the work of Michel de Certeau, whose ideas enjoy considerable traction in cultural geography but are seldom used in political geography and policy studies. An accent on the texture and feel of policy practice necessarily highlights the role of place in that practice. This, in turn, may help us with communicating geographical research beyond our own discipline.
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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 | 0.047 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.014 | 0.130 |
| Scholarly communication | 0.029 | 0.017 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".