Making sense of sensemaking: the critical sensemaking approach
Bibliographic record
Abstract
Abstract Purpose – The purpose of this paper is to revisit the oft cited but as yet not operationalized Weick's sensemaking framework, in order to provide suggested ways forward. Development of a method based on Weick's sensemaking is suggested as a starting point for a heuristic that takes into account missing elements from his original model while operationalizing (critical) sensemaking as an analytical tool for understanding organizational events. Design/methodology/approach – Following the trajectory of sensemaking, the limitations of Weick's model were discussed (i.e. failure to address power and context) and the critical sensemaking was developed as a method that takes into account agency in context. Empirical studies that apply sensemaking were discussed. Findings – It is concluded that plausibility and identity construction are key to understanding how some voices are heard over others and through critical sensemaking sense that can be made of such phenomena as the gendering or organizational culture and discriminatory practices in organizations. Practical implications – A heuristic can help people to understand the socio‐psychological properties involved in behavioural outcomes. Originality/value – Critical sensemaking builds on and operationalizes Weick's original sensemaking approach and demonstrates how it can be used in a range of empirical studies, something that Weick himself suggested was lacking.
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 | 0.103 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.008 |
| Science and technology studies | 0.011 | 0.109 |
| Scholarly communication | 0.027 | 0.031 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".