Bibliographic record
Abstract
Book clubs are typically spaces in which individuals can discuss their favourite young adult novel or interrogate controversial topics from best-selling non-fiction. At the same time, book clubs, and the literature read within, can also be used as tools of assimilation used to push political and social agendas. But what if the same book clubs that promote assimilation and conformity, privileging some literatures and forms above others, could be used as spaces to create new communities that celebrate other literatures? Book clubs can be a potential space for the discussion of lesser-known and suppressed Indigenous literatures while creating communities. However, facilitating Indigenous book clubs requires conscious planning and preparation to ensure that the book clubs engage with Indigenous literatures in an appropriate way. Additionally, facilitators, depending on the mandate, need to be in partnership with Indigenous communities to ensure that book clubs are the right program to incorporate. As such, this presentation will provide best practices for facilitating Indigenous book clubs, including topics such as determining book club mandates, selecting literatures, interpreting Indigenous texts, and creating respectful environments.
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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".