Research on Meaningful Work: Planting the Seeds for the Future
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,052 | 0,055 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,001 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,016 | 0,137 |
| Communication savante | 0,029 | 0,079 |
| Science ouverte | 0,004 | 0,020 |
| Intégrité de la recherche | 0,009 | 0,031 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».