Field Expansion and Contraction: How Communities Shape Social and Symbolic Boundaries
Bibliographic record
Abstract
To investigate how participants shape a field’s social and symbolic boundaries over time, I conducted an in-depth longitudinal study of five core and peripheral communities in the emerging nanotechnology field from the early 1980s to 2005. I show that core communities—futurists and government officials—initially expanded both social and symbolic boundaries to increase the field’s monetary and cultural resources, yet later they reversed course and contracted the field’s boundaries. I explain this shift by showing how an increase in resources enticed peripheral communities (service providers, entrepreneurs, and scientists) to claim membership in the field. Such claims created a self-reinforcing cycle—some peripheral communities enlarged the symbolic boundary of the field to grow the field, but this social and symbolic expansion threatened the identity of core communities and their ability to access resources. Core communities thus attempted to restrict the symbolic boundary and use this narrow definition to police membership claims by peripheral communities aiming to access the field’s resources. I develop a theoretical model of how debate over a field’s identity and resources shapes its social and symbolic boundaries. I show how different communities strategically manipulate field boundaries depending on their identification with the field. Core communities seek to keep the social and the symbolic boundaries aligned, while peripheral communities that identify only weakly with the field pursue their self-interested actions irrespective of whether these actions misalign the social and symbolic boundaries.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".