The Good, the Unconscious, and the Dynamic: Rethinking Disidentification at Work
Bibliographic record
Abstract
Disidentification refers to an individual’s perceived sense of separation from: (a) personal characteristics or traits (personal disidentification, e.g., not identifying as a smoker); (b) a role or relationship (relational disidentification, e.g., not identifying as a leader); and/or (c) a group (social disidentification, e.g., not identifying with an organization) (Elsbach, 1999). Together, identification (a sense of oneness) with and disidentification (a sense of separation) from targets shape an individual’s identity (Stone, 1962). Existing research has predominantly focused on the detrimental consequences of disidentification, ranging from boycott and public disparagement (Elsbach and Bhattacharya, 2001; Pratt, 2000) to workplace deviance (Bolton et al., 2012) and organizational crimes (e.g., Vadera & Pratt, 2013). However, the overwhelming focus on negative outcomes has contributed to disidentification’s receiving less scholarly attention compared to identification (Kalkman, 2023; Kreiner & Ashforth, 2004). The papers in this symposium seek to reinvigorate research on disidentification by addressing key limitations: its prevailing characterization as dysfunctional, the lack of consensus on its definition and mechanisms, and the limited exploration of its temporal dynamics (Ashforth, Harrison, & Corley, 2008). Revitalizing Disidentification Research in Organizational Studies Author: Neveen Mohamed; Vlerick Business School Author: Elise B. Jones; U.S. Coast Guard Academy Author: Nana Yaa Antwi-Gyamfi; Author: Mailys George; IESE Business School Imprisoned: Disidentification and Institutional Neurosis in Prison Staff Facing Role Conflict Author: Aušrine Vyšniauskaite; KU Leuven Author: Mailys George; IESE Business School How Workers Facilitate Clients’ Disidentification from Stigmatized Identities Author: Muhammad Aqeel Awan; London School of Economics and Political Science Author: Ussama Ahmad Khan; London Business School Author: Lidiia Pletneva; The London School of Economics & Political Science Doctors Driving Taxi Cabs: Enduring Disidentification in Downward Occupational Transition Author: Madeline Toubiana; University of Ottawa Author: Luciana Turchick Hakak; University of the Fraser Valley
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.015 | 0.078 |
| Scholarly communication | 0.020 | 0.034 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".