Bibliographic record
Abstract
Since the late 1980s a growing number of geographers and other social scientists have chronicled the apparent rise of post-Fordist economic systems (Scott and Storper 1987; Schoenberger 1988; Harvey 1989; Storper and Walker 1989; Boyer 1990; Storper 1997). These systems are said to employ a flexible approach to production reflected in employment relations, the organization of work within firms, and the broader social division of labour (Cooke and Morgan 1998). To some, the heart of this transformation lies in the rise of a new set offerees of production (Walker 1994). In particular, they point to a new set of flexible process technologies whose programmable properties offer producers prospects of great versatility, limited downtime, unparalleled precision, and superior quality. The same technologies are said to hold the potential to unleash the creative potential of workers, and to compel manufacturers to establish a new regime of co-operation on the shopfloor (Florida 1991). Despite the popularity of such arguments, their unqualified acceptance has not been universal. A critical literature has arisen which, among other things, questions the pervasiveness of such practices, especially in locations outside the paradigmatic flexible production regions (Gertler 1988; 1992; Sayer 1989; Pudup 1992). The evidence reviewed in Ch. 2 attests that, while rates of adoption of flexible technologies such as computerized numerical control (CNC) are reasonably high amongst manufacturers in countries such as the United States, Great Britain, and Canada, many firms in these countries have experienced considerable difficulty in trying to implement such technologies effectively (Jaikumar 1986; Beatty 1987; Meurer, Sobel, and Wolfe 1987; Kelley and Brooks 1988; Turnbull 1989; Oakey and O’Farrell 1992). Furthermore, the discussion in Ch. 2 also shows that there is an apparent regularity to the geography of technology adoption difficulty that is highly suggestive of its roots. Many of these implementation difficulties seem to arise in older, mature industrial regions, where manufacturing firms are far removed from the major production sites of the new flexible production technologies. Increasingly, the leading producers of these process technologies are to be found in such countries as Germany, Japan, and Italy, while once-dominant American machinery producers have seen their market shares drop significantly, both at home and abroad (Graham 1993).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".