Bibliographic record
Abstract
The present paper is concerned with the inclusion in free English-language institutional internet reference works of terms used to discuss health, disease, treatments and medical breakthroughs in the context of COVID-19. The focus is on the glossaries that are available on the websites of the UK Parliament and UK Government, i.e. on credible and authoritative platforms that are in various ways intended to serve as seats for asymmetrical transfer and mediation of knowledge about their operations and services (Engberg & Luttermann, 2014): Coronavirus (Covid-19) Definitions is the online interactive glossary published by the Office of National Statistics in 2022; the COVID-19 glossary is published by the Parliamentary Office of Science and Technology (POST). Cross-verification of wordlists and lexicographical treatment in selected dictionary entries is carried out vis-à-vis free and unlocked content from the Oxford Reference platform and the English Wiktionary. Integrating insights from the Function Theory of Lexicography (Bergenholtz & Tarp, 1995), and Wiegand’s (1977 ff.) Actional-Semantic Theory of Dictionary Form, we are able to demonstrate that Coronavirus (Covid-19) Definitions and the COVID-19 Glossary provide basic answers to the queries of lay users – which is in line with the government’s social responsibility to pursue health promotion, prevention of ill health and health protection.
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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.010 |
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".