Transforming New Ideas into Practice: An Activity Based Perspective on the Institutionalization of Practices
Bibliographic record
Abstract
Abstract We develop an activity‐focused process model of how new ideas can be transformed into front line practice by reviving attention to the importance of habitualization as a key component of institutionalization. In contrast to established models that explain how ideas diffuse or spread from one organization to another, we employ a micro‐level perspective to study the subsequent intra‐organizational processes through which these ideas are transformed into new workplace practices. We followed efforts to transform the organizationally accepted idea of ‘interdisciplinary teamwork’ into new everyday practices in four cases over a six year time period. We contribute to the literature by focusing on de‐habitualizing and re‐habitualizing behaviours that connect micro‐level actions with organizational level theorizing. Our model illuminates three phases that we propose are essential to creating and sustaining this connection: micro‐level theorizing, encouraging trying the new practices, and facilitating collective meaning‐making.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.005 |
| 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".