The Patterning of Interactive Organizational Identity Work
Bibliographic record
Abstract
Our study delves into how organizational identity work unfolds during interpersonal interactions among members of an organization. Most existing research focuses on the isolated utterances of individual members or on organizational-level discourse. Analyzing directly the interrelationships between individuals allows us to offer a fresh viewpoint on the concept of “organizational identity work.” Based on a longitudinal case study of an open innovation organization from inception, we reveal multiple patterns of interactive organizational identity work emerging over time. Although Monologue is a one-sided pattern that involves the dominance of a single voice, Polyphony involves episodes of engaged collective conversation about identity issues without clear resolution. In Dialogue, we see interactions around critical issues accumulating towards temporary compromises on identity concerns, whereas the Deadlock pattern arises when compromises appear unattainable, potentially culminating in Rupture, as interactions around identity lead members to dissociate themselves from the organization. We show how the direct and indirect focus on organizational identity issues (i.e., whether identity is the focal topic of conversation, or a consideration brought up in making another decision), as well as the intensity of personal identity engagement among participants (i.e., the degree to which the conversation addresses speakers’ personal identity commitments), may be implicated in the emergence of these patterns. Moreover, we show how the patterns in turn enact multiplicity and singularity in expressions of organizational identity within situated interactions.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| 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".