Not always invisible: finding the data about marginalized and underrepresented populations in Canada
Bibliographic record
Abstract
A sustainable data culture is also an inclusive data culture where planning, policy making, and research accounts for marginalized or underrepresented populations. Indigenous Peoples, racialized groups, and people who identify as LGBTQ+ are often underrepresented or hidden in the datasets we rely on for research and planning. Data about mental health, substance abuse, and homelessness can likewise be difficult to find, particularly for marginalized populations. To move toward a more sustainable and inclusive data culture we need to understand the historical and social context for this lack of visibility and the impact it can have in the present. It is also important to share this understanding with researchers, making them aware of potential gaps in the data, the reasons for those gaps, and alternative sources of information about marginalized groups or topics. This presentation explores the relative invisibility of several populations in the Canadian data context. We identify specific issues that contribute to these gaps including historical decisions about the Census; relationships between Indigenous Peoples and colonizing cultures; mainstream perceptions that sideline some subjects; and barriers to collecting data about certain groups or topics. The presenters draw on examples primarily from the Canadian context with occasional comparison to other international jurisdictions. In Fall 2019 and Winter 2020, two data librarians at different universities in Canada collaborated on a series of data literacy workshops about how to find data on marginalized or underrepresented populations. They will also report back on the experience of teaching these topics with their university communities and share the outcome and opportunities for providing instruction with more inclusive data sources.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".