Networks, Translation, and Collective Sensemaking as the Sources of Institutional Compromise
Bibliographic record
Abstract
We examine the role of translation in institutional maintenance. Although recent work on institutional maintenance often highlights a simplistic role of individual incumbents in protecting old institutional logics, less is known about whether and how the incumbents proactively embrace new institutional logics to enhance institutional stability. Through an in-depth, qualitative study of legal profession in Moldova after the collapse of Soviet Union, we explore how social actors taking different network positions engage in diverging translation of new institutional logics associated with Western capitalism. We find that while old institutional logics associated with communism gave way to new logics, operative institutions for the legal profession have a mixture of components from old and new logics. Our analysis also suggests that such compromised institutional arrangements are driven not only by incumbents, who more proactively engage in translation of new logics than passively resist institutional change, but also by challengers and intermediary agents who take diverse network positions. Our study therefore indicates a more nuanced image of institutional maintenance than has been suggested in prior work and highlights the value of studying how translation is collectively constructed by complex interactions between multiple actors. We conclude by discussing theoretical contributions to a broader research domain of institutional change and stability in emerging fields.
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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.046 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 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".