From Maggie to May: Forty Years of (De)industrial Strategy
Bibliographic record
Abstract
Abstract Upon becoming Prime Minister, Theresa May installed industrial strategy as one of the principal planks of her economic policy. May's embrace of industrial strategy, with its tacit acceptance of a positive role for the state in steering and coordinating economic activity, initially appears to be a decisive break with an era dating back to Margaret Thatcher, in which government intervention was regarded as heresy. Whilst there are doubtless novel features, this article argues that continuity is the overriding theme of May's industrial strategy. First, despite the reluctance to confess it, like every UK government over the past forty years, May is proposing to intervene selectively to ‘pick winners’. Moreover, the strategy envisages extending assistance to industries which have been in receipt of substantial government resources since the 1970s. Likewise, the backing anticipated for industries identified in May's strategy is dwarfed by that given to those which are not, most notably the financial services sector. Far from radically rebalancing the structure of the UK economy, May's strategy seems destined to entrench the deindustrialisation with which its governments have grappled for almost a century.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".