Igniting an Intersectional Shift in Public Policy Research (and Training): Canadian Public Policy Special Lecture
Bibliographic record
Abstract
Throughout the 2010s, a major focus of public policy and public policy debates was about understanding the sources of inequality and understanding the role of government in addressing income inequality. While progress has been made, significant gaps in inequality remain; gaps that go well beyond income inequality and which were emphasized throughout the COVID-19 pandemic. The experiences of the pandemic have served as a reminder that individuals in society have distinct experiences, and that attention to inequality and diversity needs to be seriously incorporated into modernized policy frameworks. As governments commit to a fair recovery from COVID-19, and society is more hopeful for a more just society, what is required is a much more inclusive approach to policy analysis in order to address longstanding failures of the economy and society. In particular, modernized policy frameworks need to be more representative of, and attentive to, the experiences and struggles of marginalized and underrepresented populations. Intersectionality is an analytical tool rooted in the social justice paradigm that makes clear the links between notions of identity and the systems of power through which they play out. Intersectionality considers the ways in which our identities are formed at the intersections of various social constructs, such as race, ability, class and gender, and within broader contexts and structures of power, such as the labour market and government institutions. Fully integrating intersectionality into policy analysis could create a policy analysis structure that would advance policy agendas of diversity, inclusion, and inclusive growth.
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 | 0.043 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.033 | 0.033 |
| Scholarly communication | 0.026 | 0.013 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".