Bibliographic record
Abstract
On this episodes collected, connected conversations (the sixth in our summer-long series): we get down with data and tight with tech, tackling topics that range from social media to social services. Featured voices this podcast include (in order of appearance): Kim TallBear, associate professor in the Faculty of Native Studies at the University of Alberta Ken Williams, assistant professor, University of Alberta department of drama Karyn Pugliese, Assistant Professor, School of Journalism, Ryerson University Lisa Girbav, broadcaster and podcaster Candis Callison, Associate Professor in the Institute for Critical Indigenous Studies and the School of Journalism, Writing and Media at UBC Jennifer Walker, Canada research chair in Indigenous Health at Laurentian University; core scientist and Indigenous lead with IC/ES North // CREDITS: Creative Commons music in this episode includes Headway and Harbor by Kai Engel , The Institute Laboratories and Careful now, Stalker by ROZKOL, RENDER ME - Single by Nctrnm, Robot is chilling by Frederic Lardon, Black & Blue by Breath Before the Plunge, and Sector Vector , by Little Glass Men.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.080 | 0.002 |
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; both teacher heads agree on what is shown here.
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".