Collective Sensemaking Around COVID-19: Experiences, Concerns, and Agendas for our Rapidly Changing Organizational Lives
Bibliographic record
Abstract
Uncertainty is at the forefront of many crises, disasters, and emergencies, and the COVID-19 pandemic is no different in this regard. In this forum, we, as a group of organizational communication scholars currently living in North America, engage in sensemaking and sensegiving around this pandemic to help process and share some of the academic uncertainties and opportunities relevant to organizational scholars. We begin by reflexively making sense of our own experiences with adjusting to new ways of working during the onset of the pandemic, including uncomfortable realizations around privilege, positionality, race, and ethnicity. We then discuss key concerns about how organizations and organizing practices are responding to this extreme uncertainty. Finally, we offer thoughts on the future of work and organizing informed by COVID-19, along with a list of research practice considerations and potentially generative research questions. Thus, this forum invites you to reflect on your own experiences and suggests future directions for research amidst and after a cosmology event.
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.
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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.039 | 0.050 |
| Scholarly communication | 0.024 | 0.017 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".