From Catch-and-Harvest to Catch-and-Release: Trout Unlimited and repair-focused deinstitutionalization
Bibliographic record
Abstract
Increasingly we are faced with broad societal challenges that encourage us to rethink existing institutions. Yet many people also want to preserve institutions they cherish. This tension points to the need for change that can erode or discontinue unsustainable or problematic aspects of institutions while also maintaining what is sacred and valued. In this paper we ask how can organizations deinstitutionalize taken-for-granted practices while also preserving the institution? We answer this question by exploring how Trout Unlimited deployed visual and discursive tactics to push out unsustainable catch-and-harvest fly fishing practices and insert new catch-and-release practices. Our primary theoretical contribution is a model of repair-focused deinstitutionalization, illustrating how custodians utilize three forms of work to respond to threats—mending, caring, and restoring—all with an eye on deinstitutionalization via repair rather than disruption. Importantly, we show how the construct of repair is multipurpose, not limited to maintenance strategies, but can also be a catalyst for change. In addition, we extend research on deinstitutionalization by presenting a multimodal approach that goes beyond discourse, with particular attention to visuality and show how different modalities present different affordances in longer-term repair efforts.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.058 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".