Organizations as Interpretive Systems: Exploring Opportunities for Reintegration & Reinvigoration
Bibliographic record
Abstract
Organizations have long been recognized as interpretive systems, possessing distinctive and often enduring orientations to collective sensemaking and action. However, though many streams of work provide insights into how organizations interpret the world and develop their distinctive interpretive orientations, such streams are evolving largely in separation. This panel symposium aims to resurrect an holistic notion of interpretive systems: both to synthesize insights across multiple streams of work on collective interpretation—including work on the attention-based view, sensemaking, and discourse—and to foreground opportunities for cross-fertilization. It also aims to explore the embeddedness of contemporary interpretive systems in a particular historical moment of rapid technological transformation, (digital) hyper-visibility, and a crisis in epistemic authority. In this way, the session seeks to contribute to both the reintegration and reinvigoration of an overarching interpretive model of organizations.
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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 | 0.026 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.071 |
| Scholarly communication | 0.033 | 0.037 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".