Transformation of the States’ Academic Attractiveness under the Pandemic
Bibliographic record
Abstract
This conceptual article aims at analyzing the impact of political and economic actions taken by states during the first wave of COVID-19 pandemic on their academic attractiveness for international students. The resulting crisis conditions demanded to make a lot of decisions in a very short time, thus speeding up the dynamic of the situation development and allowing to more accurately trace necessary interconnections over a short observation period.The authors focus their attention on English-speaking countries, which traditionally attract large numbers of international students: Australia, Canada, the United Kingdom and the USA. The research is based on the analysis of secondary empirical data obtained from foreign sources, as well as on official statistics. The short-term impact of political and economic decisions, made by heads of states and responsible institutions, on countries’ academic attractiveness and their perception by international students is assessed via critical and reflective analysis of surveys and researches, as well as via available data on international students’ enrollment in 2020/2021 academic year.The authors found a correlation between the decisions taken by the countries during Spring – Summer 2020 and the subsequent transformation of their academic attractiveness under the increasing competition between countries and the students’ choice of the best opportunities for their future career and life. The authors assessed the following factors affecting the attractiveness of a state as a studying destination: economic support measures taken by the governments of the considered countries during the first wave of the COVID-19 pandemic, new adjustments in visa rules for foreign students and related regulatory changes, as well as the most significant public statements by officials. The research topic can be further expanded and supplemented through data actualization, including additional factors into the analysis and expanding the time period of the study. The findings and recommendations given in the conclusion of the article can be practically applied when developing state education export strategies or universities’ and educational agencies’ recruiting approaches.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".