Editorial: Education in Times of Crisis: Methodologies and Solutions
Bibliographic record
Abstract
Special Issue publishes research articles, essays, research reports, teaching notes, and book reviews on a wide range of topics of interest to the language, literature and Education. Specifically, we encourage submission of manuscripts that, in a concrete way, apply language and linguistics or critically reflect on the application of educational technology. The aim of the Special Issue is to publish articles that contribute significantly to the body of knowledge. It publishes both theoretical and empirical articles and case studies relating to linguistics, literature, education, English, arts and humanities and related disciplines. Published articles use scientific research methods, including statistical analysis, case studies, field research and historical analysis. We call for multidisciplinary and multi-country contributions in social sciences and humanities that address the severe and global COVID-19 crisis, its impacts and its opportunities for the future. We are in the unprecedented territory, and we aim at encouraging scholars in the field to reflect and debate the role of social sciences practices in leading governmental and non-governmental organizations’ policies; in sustaining public services, businesses and not-for-profit organizations’; as well as the state of the human condition. We call for scholarly debates on how this pandemic is affected and may affect the practice of education in times of crisis and the need for accountability at a global level. The Special Issue may target scientists, researchers, professors, students and policy makers from English literature, English linguistics, teaching and learning English as a Second Language (ESL), as an Additional Language (EAL) or as a Foreign Language (TEFL) Education and methods of teaching, Literature, Languages and Information related domains.
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 | 0.015 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.030 | 0.016 |
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".