The Social Validation and Coping Model of Organizational Identity Development
Bibliographic record
Abstract
Considerable research has explored the variables that affect the success of newcomer on-boarding, socialization, and retention. We build on this research by examining how newcomer socialization is affected by the degree to which newcomers’ peers and leaders provide them with positive feedback. We refer to newcomers’ perceptions of this feedback as “social validation.” This study examines the impact of social validation from peers and leaders on the development of organizational identification over time and the turnover attitudes of new employees. We found that perceptions of social validation significantly predicted how new employees used coping strategies to adapt to their new role over time, and consequently the development of identification and turnover intentions. Specifically, increased peer social validation predicted a greater use of positive coping strategies to engage with the new organization over time, and less use of disengagement coping strategies. In contrast, initial leader validation decreased newcomers’ disengagement from the organization over time. These results highlight the role of the social environment in the workplace in temporally shaping and validating newcomers’ adaptation efforts during transitions.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".