The decent work agenda and the advancement of gender equality: for emerging economies only?
Bibliographic record
Abstract
The International Labour Organization's Decent Work Agenda offers a valuable alternative to the traditional framing of most contemporary employment regulation. It moves beyond the standard employment relationship to include workers in non-standard employment and the attainment of gender equality has a central place, illustrated in the ILO's 2009 campaign around 'gender equality at the heart of decent work'. While most OECD countries have endorsed the Decent Work Agenda (DWA), few have taken it up at the domestic level, apparently seeing it as something of benefit to emerging economies only. Our article draws on interviews with key government, employer, union and civil society stakeholders in Australia, Canada, the Netherlands and the United Kingdom, and an analysis of relevant policy documents to tease out this 'othering' of the DWA and how different understandings of gender (in)equality relate to views about its utility in the national context. We argue that assumptions that the DWA has little to offer developed economies represent a missed opportunity to rethink the gendered policy underpinnings of domestic employment regulation that are shaped by and contribute directly to gender inequality.
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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.038 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".