Geographies of Lifelong Learning and the Knowledge Economy
Bibliographic record
Abstract
With the advance of neoliberal globalization in the 1990s, lifelong learning emerged in the policy frameworks of the United States, Canada, and the EU. Neoliberal policies during this era worked to orchestrate personal development within the increasingly flexible processes of global capitalism, placing both within the rhythm of a personal life that must be fulfilled. Such an orchestration produced certain spaces—captured in notions such as the "learning society" and "creative city"—in which citizens were expected to take responsibility for their own human capital development as flexible entrepreneurs. For the majority of the population, however, this process led primarily to their own deskilling. Moreover, not only did lifelong learning strategies promote the standardization and homogenization of educational skills, and thus the abstraction and interchangeability of labor, but they were also bound up with the production of a so-called learning society that demanded increasing levels of external management. This chapter looks at some of the ramifications of these processes on workers and systems of education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".