Situation, Motivations, Cognition: Reconsidering the Fundamentals of Sensemaking
Bibliographic record
Abstract
Sensemaking is a critical organizational activity that occurs throughout an organization, from supporting leadership in making strategic decisions to underpinning the construction of organizational actors’ identities. Much of our understanding of sensemaking comes from studies of short-term, face-to-face events and assumes relatively homogenous cognitive processes across actors. Today's reality is often far removed from that, leaving us with little understanding of how individuals engage in collective sensemaking over time and physical space, and how individual motivations and cognitive differences interact to complicate that sensemaking. The goal of this panel is to (re)consider contemporary issues of sensemaking by exploring three topics: situation, motivations, and cognition. By exploring these topics, the six panelists, each leading scholars in the sensemaking domain, share their views on enduring challenges within sensemaking theory and how these might be addressed. Through group discussions and the panelists’ own thoughts, this symposium promises to further our understanding of sensemaking by exploring both the situated and psychological nature of sensemaking within contemporary organizational contexts.
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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.054 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.012 |
| 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".