Unpacking complexity: actors' work in the shaping of institutional contexts, processes, and outcomes
Bibliographic record
Abstract
This dissertation investigates the different forms in which actors’ actions and interactions influence institutional fields, processes and outcomes. Using the oil pipeline industry in Canada as the empirical context, and a large dataset of hearing transcripts from the National Energy Board of Canada, I developed three empirical studies that highlight agency and variation, rather than determinism and isomorphism. Across the three studies, I rely on the literature on institutional work, institutional plurality, institutional complexity, and performativity. These bodies of work highlight the increasing pressure that organizations face to navigate contexts in which agentic actors skillfully mobilize a variety of demands stemming from multiple notions of value. In my first study (Article #1), I take a configurational approach to unpack the notion of institutional complexity and demonstrate that “not all complexity is created equal” – actors’ actions and interactions influence the presence and intensity of different complexity-related components, which in conjunction result in distinctive types of complexity. By differentiating among types of complexity, I was able to identify the conditions under which different organizational responses to complexity (e.g., avoid, hybrid, compartmentalized, or loose coupling) were more likely to be associated with different outcomes, such as legitimacy maintenance or loss. In my second study (Article #2), I take a quantitative approach to investigate the role of emotional language in institutional work under conditions of complexity. Through a set of statistical analyses, I demonstrate that the use of emotional language is mainly an actor’s choice rather than being determined by the type of concern mobilized (i.e., the institutional prescription). Likewise, we demonstrate that relying on emotional language enhances the effectiveness of disruptive institutional work but only around certain issues – when advocating for environmental issues, while the opposite is true for social issues (i.e., less emotional language enhances disruption). In my third study (Article #3), I take a grounded theory approach to investigate how marginalized actors affect change to advance and protect their interests in institutional fields that are adverse (or hostile) towards them. The change in question is the emergence of aboriginal monitoring programs in the oil pipeline industry, which imply the recognition of the rights of Indigenous Peoples to be stewards of their territories and of the value of their knowledge systems in the identification, appraisal, and management of environmental impacts. I contribute to the literature by proposing the notion of institutional subversion as a form of institutional work through which marginalized actors can undermine notions of knowledge validity and power (or authority) governing a field in order to affect change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".