The Interplay of Reflective and Experimental Spaces in Interrupting and Reorienting Routine Dynamics
Bibliographic record
Abstract
When organization members strive to radically change routines, they face a puzzle: How can they bring about change in performances when these are guided by pre-existing ideas on how to perform the routine, that are themselves recursively reproduced? Drawing on insights from longitudinal case studies of two initiatives to change patient processes in hospitals, this paper suggests that two types of “spaces”—bounded social settings characterized by social, physical, temporal, and symbolic boundaries—are important mechanisms through which actors engage in deliberate efforts to alter both performances (performative aspect) and abstract understandings (ostensive aspect) of a given routine. Specifically, whereas reflective spaces are set apart by social, physical, and temporal boundaries and involve interactions that are geared toward developing novel conceptualizations of a routine, experimental spaces enable the integration of new actions into routine performances by locating them within the original routine, while establishing symbolic and temporal boundaries that signal the provisional and localized nature of experimental performances. As both types of spaces contribute to achieving change in complementary ways, they need to be enacted iteratively in relation to each other. The study offers a model of intentional routine change that articulates the role of spaces in interrupting and reorienting their recursive dynamics.
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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".