Bibliographic record
Abstract
Lived expertise (LE) is a valuable form of expertise that can lead to more effective policymaking. Existing research points to important mechanisms for where and how to include LE. It also offers lessons around the potential exclusionary effects such mechanisms may have. In this article, we bring the discussions together and ground them in the Canadian case of homelessness. Failures in Canadian homelessness governance and policy highlight the utility of LE where it has been included, but we also find that its prevalence is unknown. Recent mechanisms including LE are still limited and their influence is questioned. We insist that the inclusion of LE cannot be haphazard or merely a nod to its value. Rather, it requires careful and considerate inclusion that centers LE throughout the policy process, encourages its influence and innovation, and embeds mechanisms for its long-term involvement within governance structures.
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 | 0.023 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.069 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".