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Record W3163153694 · doi:10.1017/9781788213301.011

Industrial strategy for post-Covid Britain: a renewed public purpose for the state and business

2021· other· en· W3163153694 on OpenAlexaboutno aff
Suzanne J. Konzelmann, Marc Fovargue-Davies

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional resilience and development
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Quarter (Canadian coin)State (computer science)Pandemic2019-20 coronavirus outbreakFinancial crisisPerspective (graphical)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Scale (ratio)EconomyEconomicsEconomic historyPolitical scienceBusinessMarket economyHistoryGeographyMacroeconomicsCartography

Abstract

fetched live from OpenAlex

There is no doubt that the UK economy will take a big hit from the Covid-19 pandemic. Estimates of its actual impact vary, but, at the time of writing, most commentators put the figure at between 11 and 14.5 per cent. To put that into perspective, following the 2007–09 financial crisis, between the first quarter of 2008 and the second quarter of 2009 the UK economy shrank by a little over 6 per cent, and it then took five years to regain its pre-crisis size. The sheer scale of the Covid-19 impact means that the state's support for the recovery will need to include an industrial strategy – something that Britain has not seen in over 40 years. The nature and effectiveness of that strategy will largely depend upon the relationships between government and the various industrial sectors; much will also hinge on the question “What are companies actually for?”. From Margaret Thatcher's election in May 1979 until very recently, the answer was a very definite “Making profits and delivering value to shareholders”. But fallout from recent events, reminiscent of the interwar years, proves that it is just not that simple. Beyond profit: the question of corporate purpose Part of the United Kingdom's response to the 2007–09 financial crisis was to bail out financial institutions deemed “too big to fail”. The fear was that, had they not been rescued, the resulting damage would be more than purely economic. The social dislocation resulting from account holders having no access to funds, losing their savings or being unable to pay their mortgages – plus the consequences of increased unemployment caused by the “Great Recession” that followed – would have been enormous. The bailouts were therefore a de facto admission that businesses are indeed about more than profit. If that message needed any more reinforcement, it came in the form of admissions, by every government since 2008, that the economy needed rebalancing, with a new emphasis on manufacturing. We have yet to see any action, though. As it stands, the government's proposed post-pandemic recovery programme offers a lot of infrastructure investment; but, inexplicably, both industrial strategy and a business bank remain absent.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0100.014
Scholarly communication0.0250.008
Open science0.0010.008
Research integrity0.0270.020
Insufficient payload (model declined to judge)0.0160.005

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 head0.133
GPT teacher head0.270
Teacher spread0.137 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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