Then and Now: Psychological Contracts
Bibliographic record
Abstract
Over the years, the management field has had many important contributors to its theoretical development and practical application of major concepts. As a relatively young academic discipline, we are fortunate to have access to many of the pioneers responsible for its foundation, history, and evolution. The “Then and Now” program actively involves these people and provides a forum to engage with those who are following in their footsteps. “Then and Now” is an annual symposium that appeals to new and seasoned scholars across the Academy. Prior sessions have centered on goal setting (Locke, Latham & Picollo, 2011), expectancy theory (Vroom & Ellingson, 2012), leadership (Schriesheim, Gardner & Antonakis, 2013), positive OB (Luthans, Welsh & Peterson, 2014) organizational justice (Folger, Bies & Rodell, 2015), trust (Sitkin, Gillespie & de Jong, 2016), turnover (Lee, Kraimer & Halvorsen, 2017), job design (Oldham, Kulik, Wrzesniewski & Baer, 2018), and entrepreneurial orientation (Miller, Lumpkin, Wiklund & Wales, 2019). This year’s session focuses on Psychological Contracts. The symposium will begin with Denise Rousseau describing how she became interested in this topic, who collaborated/supported her, and how this construct developed. Dr. Ho will talk about her research on psychological contracts, and how it spans generations and extends ideas. The “Now” panelist Dr. Hansen will describe how her recent research on psychological contracts has evolved from the original body of work, and where it is likely to go next. The symposium concludes with audience discussion.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.025 | 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".