Bibliographic record
Abstract
History plays a significant role in preserving a nation’s cultural heritage and traditions and in shaping the collective identities of the nation’s citizens by incorporating a sense of continuity in them to identify themselves as a part of a broader historical narrative. According to some of the greatest historically significant personalities like E.H. Carr, Mark Twain, and Theodore Roosevelt, such evocative History is possible only by adding a few manipulative narratives that uphold the nation’s glory and deleting the bitter truth that distorts the nation’s glory. Such biased and fabricated historical narratives are termed as the Dominant historical narratives. As the self-explanatory term suggests, these narratives give precedence to the rulers’ perspectives and interpretations while sidelining or silencing the experiences of the Oppressed or the Subaltern. The Institutionalized Historical narratives have gained prominence, especially in Settler colonies like the United States, Canada, and Australia. In the context of Australia, its official history commenced only with the arrival of Europeans, which overlooked the existence of historical footprints of autochthonous populations. This paper strives to excavate the suppressed historical narrative of the Aborigines by closely reading Alexis Wright’s award-winning novel Carpentaria. Through this study, an attempt is made to provide a counter-historical narrative that challenges the existing authoritative Dominant colonial narrative.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.026 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".