Academic conferencing in the age of COVID-19 and climate crisis: The case of the Comparative and International Education Society (CIES)
Bibliographic record
Abstract
In this article, organisers of the annual conference of the Comparative and International Education Society (CIES), held during March and April 2020, share their story of moving the planned on-site conference to a virtual space, as necessitated by the COVID-19 pandemic. Their analysis of the vCIES (the name given to the virtual conference) process not only provides an example of a disruption to the status quo of the institution of conferencing as a result of a global pandemic, but also extends it by addressing the multiplying concerns, urgent considerations and actions needed within academic communities for more equal and accessible conferencing in the unfolding climate catastrophe. The authors begin by discussing the challenge of academic conferencing in the age of COVID-19 and climate crisis. They highlight how their decolonial political stance (which critiques accepting Western knowledge and Western culture as the norm) and their climate-conscious approach informed their preparation of a virtual conference pilot already intended as an experimental extension to this year's on-site event. They suggest the development of this pilot provided the necessary platform for transforming the vCIES into an effective and engaging virtual experience for participants. The vCIES process, including considerations concerning its structure and format and the necessary technology, is detailed in the subsequent sections. In the final part of their article, the authors briefly identify and discuss some of the opportunities, challenges and implications emerging from their vCIES experiences. Ultimately, they suggest that in a time of instability, insecurity and uncertainty, there need to be alternatives to large on-site conferences which require excessive and extensive academic mobility. The vCIES was a step in that direction as an accessible, environmentally responsive, more equal, and intergenerational and multispecies event that welcomed families, children and pets, while opening the space for new interdisciplinary encounters.
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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.050 | 0.027 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.016 | 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".