Bibliographic record
Abstract
To semiotically read – especially reading a (post)colonial cultural context like the vast Aboriginal cultural landscape in Canada – is to come face-to-face with the impossible. The impossibility of re/presentation, the impossibility of language and the impossibility of speaking as an insider. So we come clean: we are outsiders to Aboriginal culture. The first author is a young, middle-class, White woman and the second is a working-class, Black man of African descent. What we are attempting to do here is not a “cultural voyeurism” (Clifford & Marcus, 2010), but a reading wrapped with humility. It is actually not a ‘reading ’ in the Lyotardian (1993) sense of “infinite language game, ” where what Melanie Klein (1964) calls “the real ” becomes another language, where there is nothing but language. Our contention is: there are no ‘games ’ in (post)colonial contexts; “the real ” is too excruciating to be simply a “language game. ” Fully conscious of this ethics of impossibility, therefore, we can only attempt to pay homage to that which is overwhelmingly humanizing when read with humility. Yet, language is all we have. In this context, genuinely we want to ask: doesn’t language cheat us? Does it really say what we want to say? That is, as soon as we start a
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".