Capturing Real Employment Relationships: Integrating the Study of Time and Psychological Contracts
Bibliographic record
Abstract
The ability of the current psychological contract literature to accurately reflect and inform real employment relationships is severely limited because most research has ignored the dynamic nature of psychological contract related constructs and processes, the role of time-focused constructs in the study of psychological contract processes, and the temporal contexts within which these processes occur (e.g., Ng, Feldman, & Lam, 2010; Rousseau, 1995; Schalk & Roe, 2007). Answering repeated calls in the management literature (e.g., Roe, 2008, 2009; Shipp & Cole, 2015; Sonnentag, 2012), this Presenter Symposium includes a collection of five papers that acknowledge the critical role of time in the study of psychological contracts. These theoretical and empirical papers argue and demonstrate that accounting for the dynamics of the psychological contract and the role of time in psychological contract processes help the literature more accurately reflect and inform real employment relationships. The format of the proposed symposium will encourage high levels of audience participation and will offer scholars the opportunity to form new collaborative relationships. Following a brief introduction and the five 8-minute presentations, the audience members will cycle through two facilitated in-depth small group discussions as well as participate in a full group discussion on how to better capture the real employee-employer relationship.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".