Bibliographic record
Abstract
A recent YouGov survey,1 commissioned by the Royal Society of Arts during the global COVID-19 pandemic, uncovered the fact that only 9 per cent of Britons wanted to go back to the way things were before the onset of the virus. Most of those surveyed have noticed a number of positive improvements in and around their lives, ranging from the quality of food they eat and prepare, to exercise, to air quality and the apparent presence of more wildlife. In making these observations they are signalling the fact that although there have been many hardships, there have been some deep and important improvements to the quality of their lives that have been worth the inconvenience and that they would like to hang on to long after the virus has passed. This paper is intended to draw together observations of two extremely topical disruptors — global warming and COVID-19 — in an effort to inform a third: the transformation of how we go to work. There is little doubt that an unintended symbiotic relationship now exists between these three game-changers and the YouGov survey respondents have issued a note of hope that the many lessons learned during the pandemic may have opened minds sufficiently to contemplate improvement in other areas.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".