Socialization and Identification of New Recruits in Knowledge Intensive Firms: A Case Study
Bibliographic record
Abstract
Using the models from van Maanen and Schein (1977) and Dutton et al. (1994), we have been able to investigate the socialization process and organizational identification both from the organizational as well as the individual’s perspective. The link between these two perspectives establishes a better insight into the company’s actual socialization intentions and the individual’s identification with the firm. From our research it is indicated that the perceived organizational identity affects new recruits identification with the company in a strong sense. However this identification was not enhanced by the socializition tactics of either individualization or institutionalization. We have hence found factors within Alfa Laval which seemed to enhance new recruits identification and socialization with the company. These factors include a culture which is open, friendly, informal and international, and a working environment which is challenging but with full of opportunities. Addtionally a finding was that none of the new recruits could specifically state the values of the organization, but that they merely made up their own sets of perceived organizational values which they thought identified the company.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".