Bibliographic record
Abstract
Abstract This special section contributes to the growing interdisciplinary field of camp studies by examining the ways in which scholars methodologically approach and study camps and camp‐like spaces. The characteristics of camps, which render them of interest to scholarship in the first place, simultaneously generate methodological, ethical, and practical questions for research. Yet comparatively few studies have explicitly addressed the methods and methodologies in camp research. How do camp contexts shape our underlying research philosophies and how do particular ways of doing research impact our conceptualisations of camps? The contributors to this special section provide a variety of answers to these questions, drawing on empirical research in/on current and historical camp settings. Overall, we gesture towards “camp methodologies” not as a set of prescribed tools, techniques, or epistemologies to be followed when studying camps but as a shorthand for approaches that consider first, how camp geographies delimit research activities and second, how methodological choices in turn (re)construct the camp conceptually in different ways. Ultimately, this collection aims to encourage critical debates and reflections to shed more light on the methodological effects, positionalities, responsibilities, complicities, and continuing necessities of studying camps.
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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 | 0.166 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.066 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".