Institutional Settlements and Organizational Hybridity: The Rise and Fall of Supervised Consumption Sites
Bibliographic record
Abstract
Abstract In this chapter, the authors consider the relationships between institutional settlements at the field level and the instantiation of logics at the organizational level. The authors present the case of Supervised Consumption Sites (also known as Safe Injection Sites) in Alberta, Canada where a settlement of logics supported by one government was disrupted with the election of a new provincial government in 2015, and then disrupted again after the election of yet another government four years later. The authors use this case to show how different institutional settlements can support or threaten particular types of organizations, and they also show how the instantiation of different settlements in organizations (organizational hybridity) can impact the ways in which organizations present themselves. By analyzing the public justifications provided by key members of Supervised Consumption Sites, they draw attention to connections between institutional settlements at the field level and organizational attempts to manage multiple logics.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".