Bibliographic record
Abstract
Abstract Gender lends itself well to an evolutionary analysis which focuses on non-equilibrium change and transformation for individuals within society. Decomposition by such an important category as gender helps us understand the economy at the macro level, and design macroeconomic policy, better. It also provides the foundation for advocating equal gender rights and outcomes. But, where gendered policy issues arise in mainstream macroeconomics (income maldistribution, labour market composition, etc.), the subject matter is narrowed by its microfoundations, by focusing on GDP growth and on suboptimal outcomes being explained by market imperfections. An approach which takes gender seriously requires the different epistemology which arises from feminism: it does not rely on dualistic categorisations, but builds on the idea of situated knowledge, which in turn requires a pluralist methodology and an acceptance of fundamental uncertainty. Such a methodology allows for emergent identity, for the cognitive roles of emotion and social convention, and for attention to power other than market power. Reflecting on how limited is the scope for mainstream macroeconomics to address gender, and what is required of a useful alternative, a political economy approach provides a clear focus for a more general discussion of the future of macroeconomics from an evolutionary perspective.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".