Reckoning with Organizational Identity and Innovation in Research Libraries
Bibliographic record
Abstract
Who or what an organization thinks it is—its sense of identity—greatly informs the choices it makes. Yet an organization’s identity, which provides coherence and stability, may constrain or enable an organization’s capacity for innovation, which at its core is about doing new or different things. This paper explores the dynamics of organizational identity and innovation through a qualitative study involving leaders from eleven U.S. and Canadian academic research libraries—organizations and a profession that are experiencing an abundance of change and identity threats. A major finding of this research is that the very process of scoping and defining innovation can enable libraries to clarify and reckon with organizational identity dissonance: the gap between a library’s espoused identity and its identity in use. How—and to what degree—a library decides to address such gaps can further or diminish its capacity for innovation, as well as sustain or evolve aspects of its identity, over time. This research extends previous research on organizational identity and organizational learning by demonstrating that the nexus of organizational identity and innovation management constitutes a unique and robust site for organizational learning. Practical applications for research and practice in the field of academic librarianship are presented.
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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 | 0.040 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.031 | 0.042 |
| Scholarly communication | 0.023 | 0.019 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".