Bibliographic record
Abstract
Far from simply being a ‘bookend’ of the industrial age, deindustrialization is an integral part of capitalist development and thus has a long history. In the North American context, the early scholarship on deindustrialization emerged from the efforts to resist plant closures as they were happening. More recently, the field of deindustrialization studies has been reinvigorated by authors who have shifted the centre of gravity from the US Rust Belt to Europe and increasingly to other parts of the world. This introduction traces the emergence and recent transformations in the field of deindustrialization studies. It also introduces this themed issue on the politics of deindustrialization, which tells the stories of shuttered mines, mills, and factories within the wider restructuring of the international division of labour in the late twentieth century. The articles, written from a range of disciplinary perspectives, extend outward to how workers, their unions, state actors, and the general public responded to the challenge of deindustrialization. The authors are all affiliated with the ‘Deindustrialization and the Politics of Our Time’ (DePOT) research project (deindustrialization.org), which brings together many of the world’s leading deindustrialization scholars to put the field in transnational perspective. By extending the range of comparisons and scales of analysis, this special-themed issue invites us to broaden our understanding of deindustrialization, both thematically and methodologically.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.561 | 0.387 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".