Bibliographic record
Abstract
In writing about `whiteness' we are trying to enact a `way of talking' that draws in part on Aboriginal ideas about how to conduct a conversation or tell a story. We also use Homi Bhabha's ideas of `third space' (an `interruptive, interrogative, and enunciative' space) and hybridity as a related way to think through the problems of essentializing binaries and rigid identities. In Aboriginal cultures in Australia and Canada, rather than adopting the `neutral' or `objective' stance common in the academy, it is customary to introduce oneself to one's audience, providing a context to assist in interpretation and exchange. Without such an introduction, real stories cannot be told and productive conversations cannot happen. We thus begin our conversation with each other and with you by examining our personal relationship to the idea of whiteness in order to reveal some of its complexity in Canada and Australia. `Whiteness' as an abstraction has proved useful in moving the invisible norm to visibility, but we show how an awareness of `whiteness' in the two locations can be recuperated to re-privilege the already privileged. Aboriginal speakers and writers have theorized `whiteness', in many cases from outside the academy, in the process `hybridizing' traditional genres. For many of them, Aboriginality, like whiteness, is a construct that often stands in the way of thinking clearly about where to go next in the fight against racism.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.047 | 0.023 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".