Motivation of Newcomer Socialization: Integrating Multiple Perspectives
Bibliographic record
Abstract
To address the role of motivation on newcomer adjustment, the current symposium includes four studies that examine the influence of motivation during newcomers’ socialization period, including the role of job characteristics in shaping and fostering newcomer engagement, supervisors’ different orientations for supporting newcomers, supervisors’ expectations of and interactions with newcomers, and newcomers’ passion towards their work. Effects of Job Characteristics on Newcomers Psychological Presence and Engagement Presenter: Alan Saks; U. of Toronto Presenter: Jamie A. Gruman; U. of Guelph The Comparative Effects of Insider Helping Motives on Newcomer Adjustment Presenter: Alex L. Rubenstein; U. of Central Florida Presenter: John Kammeyer-Mueller; U. of Minnesota Presenter: Tomas Thundiyil; Central Michigan U. Newcomer Innovative Behavior: Factors that Enable and Inhibit Presenter: Jenny Chen; U. of the West of England Presenter: Helena Cooper Thomas; Auckland U. of Technology The Differential Role of Passion on Newcomer Socialization, Engagement, and Innovation Presenter: Junseok Song; U. of Minnesota Presenter: John Kammeyer-Mueller; U. of Minnesota
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".