Journal on Education in Emergencies: Volume 8, Number 3 (Complete)
Bibliographic record
Abstract
The research articles and field notes included in this JEiE special issue reinforce Arundhati Roy's (2020) notion that a pandemic can be considered "a portal, a gateway between one world and the next."The authors who contributed to this special issue offer insights into how education systems, students, teachers, and parents experienced the COVID-19 pandemic; how they confronted the challenges they faced; and how, in some cases, they began to conceive of a different and better future for education across the world after passing through the pandemic's portal.Reimers and Opertti (2021, 39) likewise observe in their introductory chapter of Learning to Build Back Better Futures for Education that, in response to [the COVID-19 pandemic], many stakeholders collaborated to create novel ways of sustaining education at times when this was very challenging.These efforts are important not just because of what they did at a time of great need, but because of what they show about what is possible in reimagining education.There is much to be learned from studying these [31] innovations, particularly when it comes to supporting the necessary transformation of schools and school systems around the world. 3 This special issue reflects an enormous and unprecedented undertaking by the Journal on Education in Emergencies.Our call for papers resulted in more than 200 abstract submissions and, subsequently, we received 69 theoretical and empirical research manuscripts or field notes.After a process of doubleanonymous peer review, we selected six research articles and four field notes for this special issue and commissioned three book reviews.The global nature of the COVID-19 pandemic is reflected in the scale and scope of these contributions from 37 authors in a wide range of countries.We decided to publish this special issue in French as well as English, given that major pandemics (e.g., Ebola and HIV/AIDS) occurring prior to COVID-19 impacted education and society in francophone Africa.Moreover, given the global nature of the pandemic, it was important that we expand the reach of the evidence presented in this issue beyond an English-speaking audience.We hope that, by publishing this special issue in French, we will encourage scholars in francophone countries to broadcast lessons from the fieldwork and research findings on pandemics found within this issue to their home contexts. 3Reimers and Opertti (2021) group the 31 innovations examined in their edited volume in terms of the areas these innnovations supported: (1) student-centered learning, (2) deeper learning, (3) socioemotional development and wellbeing, (4) teacher and principal professional development, and (5) family engagement.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.101 | 0.036 |
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".