Managing Identities Across Time: The Influence of Past, Present, and Future Identities on the Self
Bibliographic record
Abstract
Identities, defined as the answer to “Who am I?” (Ashforth, Harrison, & Corley, 2008), evolve in response to situational changes and proactive efforts to manage the self (Alvesson & Willmott, 2002; Sveningsson & Alvesson, 2003). Recognition of such temporality has led scholars to increasingly examine ways that past, current, and future identities might influence each other and individuals' behaviors within organizations (e.g., Eury, Kreiner, Treviño, & Gioia, 2018; Obodaru, 2017; Schabram & Maitlis, 2017; Strauss, Griffin, & Parker, 2012; Wittman, 2018). In this sense, who we are today is influenced by how we manage who we were in the past and who we would like to become. In this symposium, we explore ways that past, present, and future identities impact the self and individuals' behaviors in organizations. In doing so, we acknowledge that identities are not static and instead, are subject to influences from the past and future. This symposium provides novel insights regarding the evolvement of identities in organizations. We know relatively little about how multiple identities across different temporal domains interact to inform one’s overall vision of the self. The selection of presentations included in this symposium provide varying perspectives on ways that past, present, and future identities interact. Further, they incorporate a variety of methodological approaches (grounded theory, action research, repeated measures), including one theory presentation. We hope that this symposium will expand attendees’ understanding of identity development processes, particularly ways that identities across different temporal domains interact. Permeable Boundaries: Pre-Retirement Work Identities that Linger and Adapt Post-Retirement Presenter: Bethany Cockburn; Northern Illinois U. Identity-Shaping Systems and Emergent Worker Identities Presenter: Glen E. Kreiner; Pennsylvania State U. Presenter: Christine Anna Mihelcic; Penn State Smeal College of Business Presenter: Tiffany Dawn Johnson; Georgia Institute of Technology Who Will I Become? Presenter: Gabby Cunningham; U. of Oxford Presenter: Jeffrey Bednar; Brigham Young U. Longitudinal Leadership Transitions: Seeing Myself as a Leader (Or Not) Presenter: Christina Hymer; Darla Moore School of Business, U. of South Carolina Presenter: M Audrey Korsgaard; U. of South Carolina Presenter: Paul Bliese; Darla Moore School of Business, U. of South Carolina
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".