Meaning in Work and Meaning at Work: Empirically Based Clarity of the Constructs
Bibliographic record
Abstract
Meaning in the context of work plays a significant role in many of our lives. Yet empirically grounded clarity about what the construct signifies is lacking. In our paper, we disaggregate meaning in the context of work to meaning in work and meaning at work (Pratt & Ashforth, 2003; Wrzesniewski, 2003). Using mixed methods approach we discover the dimensions of the two constructs based on qualitative (semi-structured interviews) and quantitative (survey and CFA) studies, develop robust scales to measure meaning in work and meaning at work, and probe the relationships between the two constructs based on qualitative (semi-structured and structured interviews) and quantitative (survey and CFA) studies. Our results clearly indicate that the empirically supported main dimensions of meaning in work are fulfilment, connection, perceived importance of the work and purpose of the work; that meaning at work is represented by a wider set of dimensions than we had envisaged based on the existing literature; and that the relationship between meaning in work and meaning at work is interesting and dynamic with several nuances. We conclude our paper by discussing the theoretical and empirical contributions, the managerial implications, and the future research areas.
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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 | 0.037 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.008 | 0.059 |
| Scholarly communication | 0.016 | 0.028 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| 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".