Bibliographic record
Abstract
Self-disclosure, as the act of sharing personal information with others, has been recognized as a key mechanism for building interpersonal relationships. In organizational settings, self-disclosure is receiving increasing attention for its potential to influence work dynamics, relationships, leadership practices, and career outcomes. This symposium explores the processes and outcomes of self-disclosure in the workplace, focusing on how intentional or unintentional personal revelations about different topics impact work relationships and well-being, leadership perceptions, and career trajectories. Specifically, we will explore the decision-making processes involved in self-disclosure, the potential risks and benefits associated with revealing personal information, and how these disclosures shape perceptions of leaders, followers’ responses, interactions, and well-being at work and career advancement through hiring decisions. The five papers in the symposium include a variety of methods and designs across lab and field settings, in different countries, and regarding different topics of disclosure. Methodologies include a systematic review, between and within participants’ experiments, including the use of participant eye-tracking, qualitative, and survey-based studies. Self-disclosure topics include fertility treatment and, specifically, IVF disclosure, family and work role-based disclosure, and unintentional disclosure of marital and financial status. Outcomes of self-disclosure include perceptions and evaluations of leaders, followers' empathic concern and helping behaviors toward leaders, discloser’s well-being, demand-resource disequilibrium, and career outcomes, such as hiring and salary decisions. Together, the presentations in this symposium advance our understanding of the processes and outcomes of self-disclosure in organizational settings. A Critical Review of Self-Disclosure at Work Author: Alison Legood; Author: Yaxin Zheng; Author: Allan Lee; The Power of Supervisor Compassion in Response to Employee IVF Disclosure Author: Angela R. Grotto; Montclair State University Author: Carolyn J. Winslow; Author: Reut Livne-Tarandach; Manhattan College Reveal Hidden Roles: Effects of Leaders’ Work and Family Role Disclosure Author: Sunghyuck Mah; Seoul National University Author: MINJU OH; Author: Minjun Yoo; Seoul National University Author: Seokhwa Yun; Seoul National University Disclosure Decisions of Women Undergoing Fertility Treatment Author: Nada Basir; University of Waterloo Author: Serena Sohrab; Ontario Tech University Should it Stay, or Should it Go? Applicants’ Over Disclosure Processes in Employee Selection Author: Yochi Cohen-Charash; Baruch College of the City University of New York Author: Jonas Sutphin; The City University of New York Author: Kaitlin Busse; Baruch College of the City University of New York Author: Yuliya Cheban-Gore; The City University of New York Author: Manuel F. Gonzalez; Montclair State University
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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".