Bibliographic record
Abstract
The past thirty years have been challenging ones for traditional practices of economic development. After decades of uninterrupted economic growth coupled with generally rising living standards, economic expectations about work and wages are changing in the largest economies. Many Western European countries struggle with high unemployment rates. Japan is facing a crisis in core domestic industries and a weakening in lifetime employment policies. The US and Canada, with more flexible labour markets, are experiencing declining wages for most of their populations. The balance between growth on the one hand and rising living standards and economic stability on the other, central to the social contracts of these societies, has been disrupted by a series of changes within these economies since the 1960s. These changes can be analysed along several key dimensions: The first of these dimensions is technological. The developed economies appeared, by the mid-1990s, to be entering a major period of economic growth associated with the maturing of information technology. The information technology paradigm has now dramatically expanded within the economy, improving the productivity of a far wider range of activities. In terms of regional economic development, this level of technological change offers new opportunities for diversification, but also challenges regions to adopt the latest technology or risk losing competitiveness.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.262 | 0.155 |
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".