Sensemaking at work: meaningful work experience for individuals and organizations
Bibliographic record
Abstract
Purpose – The purpose of this paper is to take a closer look at the concept of meaningful work experience for individuals and organizations, and discuss the role of sensemaking in creating it. Design/methodology/approach – The main argument of the paper is that sensemaking efforts are among the fundamental tools that help create meaningful work experience for both individuals and organizations. The paper offers a conceptual framework that presents the interplay between sensemaking tools and enabling mechanisms in relation to internal and external organizational environments. Findings – It is proposed that job crafting is a sensemaking tool – enabled by empowerment – for individuals to make sense of the internal environment of the organization; and strategy crafting is a sensemaking tool – enabled by organizational learning – for organizations to make sense of the external environment of the organization. Originality/value – This paper attempts to converge micro- and macro-level concepts by bringing together individual- and organizational-level variables into a joint discussion. It places job crafting and strategy crafting in the context of sensemaking theory, and it reinforces the idea of proposing models that will consider the multi-level implications of organizational research.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".