Women and Employment Insurance in Canada: A Gendering-Based Assessment
Bibliographic record
Abstract
Gendering-Based Assessment (Bacchi and Eveline 2003) is used as a conceptual framework to examine the shortcomings of gender mainstreaming (GM) in relation to employment insurance (EI). First, the paper defines feminism and provides a historical overview of how GM became the conventional and dominant approach in Canada. Second, the paper explains and re-defines the problem of the EI program, and describes how EI has changed in the context of neoliberalism. By exposing the flaws inherent in neoliberalism, the paper questions the effectiveness of Canada's gender-based analysis (GBA) and argues that a deeper approach is needed to bring about systemic change. As an analytical tool, GBA does not have the capacity to guide the policy design and decision-making process in a direction that understands why EI eligibility requirements impact men and women differently. Moreover, the essay asserts that the problem with conventional approaches is that they place the difference in women, thereby disregarding the potential that policies may have in creating women, as opposed to just impacting them.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".