Bibliographic record
Abstract
E arly in the spring of 2020, we had to make the difficult decision to cancel our graduate conference, then titled "Changing Conversations: Canada in a Shifting Landscape." We could not have foreseen that, for the next 18 months, we would collectively feel and witness all sorts of shifting landscapes and that the world as we knew it would be completely transformed.In a few short months, the COVID-19 pandemic exposed the flaws and cracks in many parts of our (and others') political, medical, and social systems.We also became acutely aware that the mental health consequences of the pandemic, yet to be fully explored or discovered, will be felt for years to come.At times like these, how could we sensitively return to engaged scholarship?We first had to find a theme that spoke to each one of us on the organizing committee.Ultimately, we felt that "Canada in Conversation: Crisis, Challenge, and Change" would give students both a space to be heard and the comfort of common shared experiences.In the spring of 2021, the Robarts Centre for Canadian Studies hosted its annual graduate student conference online.Over the course of four Fridays, 29 students from universities across the country presented their work and engaged in critical exploration of our chosen conference themes.
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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.140 | 0.063 |
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".