What Do I Think of You? Others’ Perceptions in the Study of Work and Family Intersections
Bibliographic record
Abstract
Across four unique empirical papers from different parts of the world, we will explore empirical research on others' perceptions of a focal individual's work-family (WF) experiences. Scholars have used others’ perceptions of WF 1) as a methodological tool to enhance the rigor of model testing, and 2) more theoretically, as a means to understand the quality and outcomes of relationships between people at work and at home. Because others’ WF-related perceptions is now a burgeoning research area, we propose this symposium as a way of 1) naming this heretofore rather segmented area of scholarship under the same topical umbrella, and 2) beginning to take stock of findings from this sub-area of work and family research en route to building new research agendas. My Experience or Your Perception? A Meta-Analysis on the Association Between Self- and Other.... Author: Nina Junker; Institute of Psychology, U. of Oslo Author: Sharon Toker; Coller School of Management, Tel Aviv U. Author: Kinga Bierwiaczonek; U. of Oslo Author: Jenny M. Hoobler; NOVA School of Business and Economics Capitalizing on Care Author: Jakob Stollberger; Vrije U. Amsterdam Author: Mireya Las Heras; IESE Business School Author: Yasin Rofcanin; School of Management, U. of Bath Author: Vera M. Schweitzer; U. of Cologne More Motivated to Help Male Leaders? Explaining Fatherhood Bonuses via Follower Helping Author: Jamie L. Gloor; U. of St. Gallen Author: Susanne Helena Braun; Durham U. Author: Jenny M. Hoobler; NOVA School of Business and Economics (Dis)Similarity Between Workplace and Family Relationship Environment Author: Emma Lei Jing; NEOMA Business School Author: Birgit Schyns; NEOMA Business School Author: Jeffrey Yip; Simon Fraser U.
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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 | 0.063 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".