Boundary Work among Groups, Occupations, and Organizations: From Cartography to Process
Bibliographic record
Abstract
This article reviews scholarship dealing with the notion of “boundary work,” defined as purposeful individual and collective effort to influence the social, symbolic, material, or temporal boundaries, demarcations; and distinctions affecting groups, occupations, and organizations. We identify and explore the implications of three conceptually distinct but interrelated forms of boundary work emerging from the literature. Competitive boundary work involves mobilizing boundaries to establish some kind of advantage over others. In contrast, collaborative boundary work is concerned with aligning boundaries to enable collaboration. Finally, configurational boundary work involves manipulating patterns of differentiation and integration among groups to ensure that certain activities are brought together, whereas others are kept apart, orienting the domains of competition and collaboration. We argue that the notion of boundary work can contribute to the development of a uniquely processual view of organizational design as open-ended, and continually becoming, an orientation with significant future potential for understanding novel forms of organizing, and for integrating agency, power dynamics, materiality, and temporality into the study of organizing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".