Bibliographic record
Abstract
At the time of writing this article, the novel Coronavirus (code name COVID-19) claimed 203,307 human lives and left 2,923,125 affected with the virus among which 837,323 recovered, around the globe [1]. Although every sector of our life is badly affected by the COVID-19, the long-lasting effects of this pandemic will set new priorities for the nations' policy-makers. At this point, the contours of the pandemic are opaque but it is anticipated that it will take an unprecedented amount of time, effort, resources, compromises, trade-offs, and policies to set the path to the next normal. Among other walks of life, the world economy took a huge hit due to lockdowns imposed by the international communities. COVID-19 has badly affected our daily lives, shaken the healthcare systems, and above all, paralyzed the norms of work ethics due to both ‘work from home’ and ‘no work’. The way we are struggling to work remotely to keep our jobs, remote schooling, managing our personal and social lives, COVID-19 is no longer just a health or a well-being threat. It has a far bigger threat vector than we currently anticipate. Also, thanks to our globalized society and interdependent economy, this pandemic has no political, geographic and religious boundaries, and has caused a regional and global crisis (public health included). In this vein, this article is a minute attempt to shed light on the threat vector of the COVID-19 pandemic.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".