Bibliographic record
Abstract
The study of the psychological contract (PC) has received much attention over the years because it offers a unique framework for understanding attitudes and behaviors in performing work and changes in aspects such as loyalty, commitment, job satisfaction and identification with the organization (Rousseau, 1995; Zacher and Rudolph, 2021). The CP dimensions divided into two main categories are the transactional contract and the relational contract. The study mentions that psychological contracts are dynamic and not static. Also, the perception of the psychological contract may differ between the private and public sectors due to the different expectations and obligations that exist in these environments. To better understand the dynamism of the concept, our study retained two main measurement tools for evaluating CP in the professional environment. The first is Rousseau's Psychological Contract Index, which was first proposed in 1990 and updated during the period of 1998-2000. The second measurement tool is that proposed by Rogard and Becerra (2015) which makes it possible to evaluate the content of the CP of public sector agents and to measure the degree of achievement of the expectations of public sector agents. Keywords: psychological contract; transactional contract; relational contract; expectations-obligations
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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".