Gender Analysis of the New Federal Framework for Aboriginal Development: Discussion Guide and Annexes
Bibliographic record
Abstract
[...]it outlines ways in which the FFAED can integrate a gender-sensitive, cultural approach to economic development and demonstrates the value-added benefits of using a gender analysis to inform programming decisions. Key finding of the analysis offer the following conclusion: * Aboriginal women and men experience different conditions in life and as a consequence women will have significantly different access than men to the resources and benefits offered by the Framework. * The three spheres for development (Activation, Base, and Climate) proposed in the Framework should be strengthened to be more inclusive of women. * Research and analysis on existing structures is necessary to formulate the foundation for understanding how women are impacted by economic development. * Revisions to policies, strategies and options that gave rise to the Framework can help establish a culturally-based, gender sensitive approach. The Access and Control Framework is based on an analysis of the division of labour by sex in the reproductive, productive, and community spheres of the economy and on an analysis of the differential access that women and men have to the resources and benefits involved in the economic development process. The first is that this economic development needs to be gender-sensitive, meaning that any approach to economic development needs to be based on and informed by a sound understanding and prior analysis of the differences between Aboriginal women and men's socio-economic conditions and challenges in life and in the economy, and the nature of their respective contributions to the economy.
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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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".