Long-term Dynamics of Employee Identification with an Organizational Unit
Bibliographic record
Abstract
This study presents the first framework for understanding the long-term dynamics of employee identification with an organizational unit. Similar research generally adopts cross-sectional quantitative methods that insufficiently explain the long-term dynamics of identification. Following studies that find stronger identification with lower-level groups than with an entire organization, we examine the case of a marine hull unit of a Japanese property-casualty insurer. We suggest that employees initially identify with the unit because of “occupational ability recognition” and “affective recognition”. We identify that these factors are necessary and sufficient conditions for identification with the unit. When they are absent, dis-identification occurs. If identification based on these factors is maintained, “value preferences” become dimensions of identification. Members prefer the unit values of interesting work, professional utility, and career attainment. When employees are dissatisfied with the unit’s values, ambivalent identification occurs. Ultimately, mature employees may express a “value proposition orientation”. They identify with the unit by consciously relating their value proposition and the unit.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".