MétaCan
Menu
Back to cohort
Record W4400992193 · doi:10.69554/xgka8024

Global warming, COVID-19 and a new world for conducting business

2020· article· en· W4400992193 on OpenAlexaff
Chris Hood

Bibliographic record

VenueCorporate real estate journal · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsWorkplace Health, Safety and Compensation Commission
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakGlobal warmingBusinessClimate changeVirologyMedicineOutbreakBiologyEcologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.332
GPT teacher head0.333
Teacher spread0.001 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCorporate real estate journalSame topicCOVID-19 Pandemic ImpactsFrench-language works237,207