Bibliographic record
Abstract
It is exciting to publish the 2023 spring/summer issue of the JCRI, our first as co-editors.Each contribution to this issue reinforces the important work of JRCI in encouraging critical inquiry into issues of race and racialization as it unfolds and intersects with other identity constructs-class, caste, gender, ethnicity, nationality/citizenship, religion.The contributions invite us to ask what, how, when, and why racialized peoples are impacted by social and political spaces of power and to consider how folks think through, organize, and resist through a critical race lens.From the most intimate orientations-naming and misnaming-to Yao's identification of "unfeeling" as an affective lens through which exclusion and inclusion are practiced, this issue asks us to think about and raise questions about presence, voice, articulation, claim, and re-presentation.How might we imagine and work towards a future through the powerful lens offered by critical race theory?Though CRT originates from Black scholars and thinkers in the legal arena in the U.S., it is clear that we continue to benefit from CRT in learning about the lives of Indigenous, Black, and racialized people in Canada, too.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".