A dynamic phase model of psychological contract processes
Bibliographic record
Abstract
Summary In formulating a dynamic model of psychological contract (PC) phases, this paper offers new insights by incorporating a temporal perspective into the study of the PC. Although conceptualized as a dynamic construct, little empirical attention has been directed at how PCs evolve and change over time. Moreover, conceptualization of the PC and its processes has undergone limited revision since the 1990s despite challenges to some of its tenets and advances in related fields that suggest the importance of time to such processes. In this article, we address limitations in existing theory, clarify the conceptualization of the PC, and bring dynamism to the forefront of PC theory building by emphasizing dynamic processes. We propose a phase‐based model of PC processes (intraphase and interphase) wherein the functions of key variables (e.g., promises, inducements, contributions, and obligations) change over time and context. These phases include creation, maintenance, renegotiation, and repair. This model directs attention to the dynamic nature of the PC, drawing on contemporary evidence regarding self‐regulatory mechanisms. Finally, we present the implications of this dynamic phase model for theory and research.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".