Balancing Acts: Navigating Power, Care, and Complexity in Workplace Relationships
Bibliographic record
Abstract
Workplace relationships are central to organizational life, encompassing connections such as mentoring, leader-follower dynamics, and interactions with colleagues or clients. These relationships provide vital resources and emotional support, foster growth, and enhance employee well-being, job satisfaction, and performance. While extensive research highlights the benefits of high-quality workplace relationships, much of it focuses on fully formed connections, often overlooking the micro-level processes that underpin their formation and maintenance. This symposium explores these underexamined dynamics by addressing how workers navigate everyday interactions, adapt relational strategies across contexts, and manage the complex interplay between work and personal lives. The presentations draw on qualitative research, offering nuanced insights into relational processes through interviews, participant observation, and ethnographic methods. By focusing on contexts with flexible relational norms and examining a range of workplace connections—including worker- client, manager-worker, and peer relationships—this symposium seeks to illuminate how and why workplace interactions unfold and the consequences they produce. Through these diverse studies, we aim to stimulate discussion and broaden the understanding of how people form, sustain, and adapt workplace relationships, contributing to a richer understanding of relational dynamics within organizations. Navigating relational liminal space with clients in the context of caregiving Author: Solomiya Draga; Harvard Business School Navigating institutional norms and practical concerns in high-stakes settings Author: Amelia Hawbaker; University of Oxford Recognition and relational work as a managerial response to economic constraints in low-wage work Author: Julie Yen; Boston University How managers repair worker and client disruptions arising from the distribution of authority in gig Author: Laura Lam; University of Toronto How parents reconstruct work life to manage challenges from children’s mental health issues Author: Siyin Chen; Author: Qiusi Yan; The Hong Kong University of Science and Technology Author: Winnie Jiang; INSEAD
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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.056 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".