Beyond Text: Constructing Organizational Identity Multimodally
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it