Strategies for the Resolution of Identity Ambiguity Following Situations of Subtractive Change
Bibliographic record
Abstract
This article applies the concept of identity ambiguity to the individual level of analysis, suggesting that identity ambiguity will likely follow certain scenarios of change in which an essential target of one’s identification is abruptly lost. This temporary absence of identification has thus far been understudied, and it is proposed that individuals perceive it as a negative experience, in which opportunities for reidentification are uncertain and unclear. It is further proposed that they will be driven to overcome this identity ambiguity through one of four distinct strategies. The choice of which strategy to adopt is moreover said to be influenced by the interaction between opposing factors: the strength of one’s organizational identification prior to the change and the degree to which the new setting is perceived as prestigious, distinct, and with values that are congruent to one’s personal values. Finally, a discussion of theoretical and managerial implications is provided.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".