Bibliographic record
Abstract
This paper examines the meanings of the trans-tribalism in Sherman Alexie’s Face, a collection of poetic proses. By the trans-tribalism I mean the combination of Inderpal Grewal’s transnationalism in Transnational America and Shari M. Huhndorf’s transnationalism in Mapping the Americas. Grewal shows how the people from India succeed in getting adjusted to multicultural America, making themselves transnational, without yielding their inherited Indian culture at all. Huhndorf exemplifies specific ways how Native Americans in Canada and arctic areas globalize their indigenous culture by making cultural compromises with the surrounding alien cultures. With the help of the two theoreticians, I elaborate proper meanings of Alexian versions of transnationalism, which I name trans-tribalism, by analyzing six selected works. “Tuxedo with Eagle Feathers” suggests the hybridity between Native American art and American luxury clothing will stimulate Indians’s understanding of American capitalism. “Inappropriate” tells the American dream mythology can be applied even to Native Americans, teaching themselves they are indigenous immigrants. “Vilify” maintains the necessity of making new definitions of heroes, Indian and whites alike. “Bird-Organ” indicates the inefficacy of Indian authenticity. “On the Second Anniversary of My Father’s Death” and “On the Second Anniversary of My Father’s Death” point out how Indian fundamentalism and traditions are too out-dated to navigate Indians in the 21st century.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".