Research on Meaningful Work: Planting the Seeds for the Future
Bibliographic record
Abstract
The last two decades have seen a significant uptick in research on meaningful work, defined as work that is purposeful and significant. Prior work has established the link between experienced meaningfulness and positive organizational and employee outcomes, revealed how workers can make their jobs more meaningful, and illuminated numerous downsides of experiencing one’s work as meaningful. Recent reviews highlight that meaningful work has become a central topic in the organizational literature. At the same time, these reviews also highlight several limitations that currently hold the field back, including a predominant focus on calling orientations, an assumption that work orientations are static, a lack of standardized definitions and measures, and limited generalizability. Having now firmly established its place in the organizational literature, we believe it is time to “take stock” of where we are and, with a thought to addressing these limitations in mind, set the foundation for the next generation of meaningful work research. This symposium aims to take a step toward addressing this gap. It features the work of 13 early career researchers whose work begins to build on and move beyond these limitations. Guided by experienced scholars who will act as discussants, we hope this forum will encourage dialogue that will guide and enhance the next generation of meaningful work research. By showcasing diverse methods and topics, we also aim to attract scholars beyond the meaningful work community, fostering new perspectives and integrating them into the field. Work Meaningfulness During a Merger Author: Yuna Cho; HKU Business School, The U. of Hong Kong Author: Winnie Jiang; INSEAD Author: Lucas Dufour; Toronto Metropolitan U. How, why, and with what consequence passionate nurses cope with promotion out of meaningful roles Author: Solomiya Draga; U. of Toronto A change is gonna come: How life events shape changes in work orientation Author: Greg Fetzer; U. of Liverpool Author: Elise B. Jones; US Coast Guard Academy Finding and Feeling Meaningfulness in an Invisible Occupation Author: Luke Hedden; U. of Miami There’s Always More You Can Do: The Perils of Being Too Passionate for Work Author: Kai Krautter; Harvard Business School Author: Wen Wu; Beijing Jiaotong U. Collective mental time travel as a way to unite dispersed stakeholders addressing grand challenges Author: Yuxin Lin; U. of Arizona Self-Imposed Constraints in Meaningful Work: The Role of Constraints and the Agency to Craft Them Author: Justine Murray; Harvard Business School Author: Kira Franziska Schabram; U. of Washington Author: Jon Michael Jachimowicz; Harvard Business School Thwarted Prosocial Impact in Organizations: Consequences, Mechanisms, and Boundary Conditions Author: Jordan Nielsen; Purdue U. Author: Daniel Goering; Missouri State U. Let My People Go Hunting and Gathering: The Meaning of Work in Rural Alaska Author: Shawn Xiaoshi Quan; U. of Washington Author: Kira Franziska Schabram; U. of Washington How Role Archetypal Narratives Shape the Experience of Meaningfulness Amidst Distress Author: Benjamin Alan Rogers; Boston College A Tripartite Approach to Meaningful Work: Examining Purpose, Significance, and Coherence Author: Sarah Ward; U. of Illinois at Urbana-Champaign Author: Vlad Costin; U. of Sussex Meaningful Work Ideology Theory (MWIT) Author: Molly L. Weinstein; Northwestern U. Author: Eli Finkel; Kellogg School of Management, Northwestern U. Pursue Your Higher (And) Lower Calling? A Construal Approach to Calling Orientation Maintenance Author: Hannah Weisman; Harvard Business School Author: Haoyue Zhang; Nanyang Business School, NTU Singapore Author: Stuart Bunderson; Wash U.
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 | 0.052 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.016 | 0.137 |
| Scholarly communication | 0.029 | 0.079 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.009 | 0.031 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".