Beyond Text: Constructing Organizational Identity Multimodally
Bibliographic record
Abstract
Organizational scholars have proposed a broad range of theoretical approaches to the study of organizational identity. However, empirical studies on the construct have relied on text‐based organizational identity descriptions, with little exploration of multiple intelligences, emotions and individual/collective identity representations. In this paper, we briefly review the empirical literature on organizational identity, and propose a novel method for empirical study involving structured interventions in which management teams develop representations of the identities of their organizations using three‐dimensional construction toy materials. Our study has five main implications. By engaging in a method that draws on multiple intelligences, participants in this study generated multifaceted and innovative representations of the identities of their organizations. The object‐mediated, playful nature of the method provided a safe context for emotional expression. Because it involved the collection of both individual and collective‐level data, the technique led to collective constructions of highly varying degrees of ‘sharedness’. Finally, the organizational identity representations integrated unconscious or ‘tacit’ understandings, which led to the enactment of organizational change.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".