Stronger Together: Positive Relationships at Work
Bibliographic record
Abstract
The workplace in the 21st century has changed dramatically, as the internet connected the world, the “gig economy” changed work arrangements, globalization expanded the job market and individual work values such as autonomy and freedom became dominant (Rabenu, 2021). Just a few years ago, automation and technology were the heart of the conversation on the modern workplace and seemed to be replacing meaningful human connections. However, the COVID-19 pandemic, that required social distancing, demonstrated just how much people still need each other, and how technology cannot replace human relationships. The Positive Organizational Scholarship (POS) movement that started in the early 2000’s seeks to understand the role of positive relationships at work. The place of work in our lives makes it a source of meaning, purpose and identity-building, that are often created through positive relationships (Ragins & Dutton, 2007). For example, high-quality relationships enable human flourishing that in turn benefits the organization (Dutton & Heaphy, 2003). Another notable outcome of the movement is the Reflected Best Self Exercise (Quinn, Dutton, Spreitzer, & Roberts, 2003) that helps understand individual strengths through feedback from significant others. The purpose of this symposium is to contribute to understanding the role of positive relationships in the workplace. This collection of papers explores different types of work relationships with leaders, coworkers and work friends to examine how positive emotional connections help overcome challenges and promote well-being. By looking at processes on different levels of analysis, this symposium offers a broader perspective on workplace relations and unique roles they can play for employees and for organizations. I’ll Stand by You: How Leaders Can Support Employees During a Pandemic Presenter: Mirit K. Grabarski; Lakehead U. Presenter: Maria Mouratidou; U. of Cumbria, UK LMX Differentiation and its Political Effects in the Context of Performance Appraisal Presenter: Silvia Dello Russo; Luiss U. Presenter: Atieh S. Mirfakhar; Instituto U. de Lisboa (ISCTE-IUL), Business Research Unit (BRU-IUL) Presenter: Alison Legood; U. of Exeter Business School The Effects of Intersectionality on Evaluations of Interpersonal Citizenship Behaviors Ratings Presenter: Natalie Schneider; U. of Wisconsin, Milwaukee Presenter: Xiaoxia Zhu; U. of Wisconsin - Milwaukee Presenter: Megha Yadav; U. of Wisconsin, Milwaukee Presenter: Belle Rose Ragins; U. of Wisconsin, Milwaukee Disappoint Friends or Downplay Organizational Norms?The Influence of Workplace Friendship Trajectory Presenter: Kun Wang; UCL School of Management Silver Lining: Unit Cohesion Offsets the Influence of Safety Concerns on Thriving at Work Presenter: Olivier Wurtz; ESCP Business School Presenter: Mihaela Dimitrova; WU Vienna Presenter: Mila Borislavova Lazarova; Simon Fraser U. Presenter: Margaret A. Shaffer; U. of Oklahoma
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".