In Pursuit of Meaningfulness: The Job and Career Choices of Management Academics
Bibliographic record
Abstract
Work is an important component of many people’s lives. Meaningful work “is personally significant and worthwhile” (Lysova, Allan, Dik, Duffy, & Steger, 2019: 375; see also Martela, Gómez, Unanue, Araya, Bravo, & Espejo, 2021, para 2). Individuals experience their work as meaningful when it is intrinsically enriching and enjoyable and enables them to connect with or serve others (Bailey, Madden, Alfes, Shantz, & Soane, 2017; Lepisto & Pratt, 2017). The roles people play, the tasks they perform, their interactions with internal or external stakeholders, and the organizations that employ them can contribute to their perception of work as meaningful (Bailey et al., 2017). Meaningful work is associated with higher job satisfaction and engagement, organizational commitment, and performance, and with lower rates of absenteeism and turnover (Bailey, Yeoman, Madden, Thompson, & Kerridge, 2019). Academics expect and seek meaningful work (Dunn & Halonen, 2018; McDaniel, 2019). This proposed panel symposium considers how management academics can realize more meaningful work by proactively crafting their jobs and careers (Tims & Akkermans, 2020), as well as by taking advantage of fortuitous opportunities (Kindsiko & Baruch, 2019). The symposium is designed to elicit and integrate the experiences and perspectives of four seasoned faculty members in a moderated discussion that would generate insights as to how management academics can make choices that enhance their experience of meaningful research, teaching, and service.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".