The ‘Canadian Diversity Model’: A Repertoire in Search of a Framework." Ottawa: Canadian Policy Research Networks
Bibliographic record
Abstract
convened a long-planned Roundtable to discuss this paper on the Canadian Diversity Model. For those who came from outside Ottawa, many were taking their first flight since air travel resumed. We had much to share as we gathered around the table, and all the issues raised in the paper had an urgency and poignancy that far surpassed anything we could have imagined as we began the work a year earlier. CPRN had been working with the Canadian Identity division of Canadian Heritage to explore the complex issues that surround the Canadian diversity model. In addition to Canadian and international commitments to human rights, this model rests on three key pillars: linguistic duality, recognition of Aboriginal peoples ’ rights, and multiculturalism. The object of the paper and the Roundtable are to help Canadians debate and decide how to respect such cultural diversity while maintaining the cohesion necessary to sustain Canada into the 21st century. September 11th clearly adds new challenges to social cohesion, for the diversity model itself is forged out of the tensions among competing values. The need to take into account a range of values makes well-functioning and inclusive democratic institutions absolutely key to the success of the Canadian diversity model. Jane Jenson and
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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.036 | 0.038 |
| Scholarly communication | 0.025 | 0.014 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".