Romantic relationships at work: old issues, new challenges
Bibliographic record
Abstract
Sex and romance develop in offices because that's where the people are. Men and women … are likely to get together in ways not mentioned in the corporate policy manual. (Horn and Horn, 1982: 83) Over the past several decades, increasing numbers of individuals have been meeting their significant others at work. This means that in addition to professional relationships and social friendships in the workplace, romantic relationships are adding another dynamic into workplace interactions. Indeed, conditions in today's workplace are such that romantic relationships may well be inevitable. Given this, managers can take one of two approaches. The first and most frequent approach is to focus on preventing such relationships and their potentially negative consequences. However, a more recent development in the organizational literature provides a new perspective for how organizations view their employees; positive psychology and positive organizational behavior suggest that work experiences can promote mental health (Turner, Barling, and Zacharatos, 2002). Thus, the second approach changes the practitioner's primary focus from the costs of romantic relationships at work to include potential benefits. At a time when organizations are increasingly focusing on employee health, organizations may find ways to promote positive mental health gains (and limit any damage) for their employees through supporting romantic relationships. Consistent with this new perspective, the purpose of this chapter is twofold.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".