Meta-organizing on the fly in times of crisis: The emergence and morphing of COVID-END
Bibliographic record
Abstract
Meta-organizations are created to address issues of common concern that require collaboration from various organizational actors. In crisis situations, the bringing together of independent organizations with different roles could be important to support crisis response. In this paper, we draw on a real-time qualitative case study, that of COVID-END (i.e., the COVID-19 Evidence Network to support Decision-making), to examine how and why meta-organizing might emerge, evolve and dissolve (or not) during the lifecycle of a crisis situation. We show how in the midst of a highly volatile crisis, organizations dedicated to promoting evidence-based knowledge were able to coalesce around a common goal, despite prior failed attempts to create a form of collaboration. We call this process meta-organizing on the fly and trace its development over time through periods of emergence, shapeshifting and transition that imply different forms of identity work, boundary work and practice work. We contribute to the literature by showing how an environmental shock can alter the motivational landscape for meta-organizing suddenly and intensely, and we reveal the critical, yet paradoxical role of centralized leadership in enabling it to take form and potentially sustain itself. While inter-organizational rivalry may re-emerge over time, we suggest that meta-organizing on the fly nevertheless has the potential to lead to longer term transformation of organizational relations towards enhanced collaboration, and the recreation of other meta-organizational forms.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".