Museums and Materials: Ekphrasis in<i>Books and Islands in Ojibwe Country</i>
Bibliographic record
Abstract
Louise Erdrich begins Books and Islands in Ojibwe Country with a uniting sentence: “My travels have become so focused on books and islands that the two have merged for me. Books, islands. Islands, books” (3). From this opening to her memoir, she narrates a journey with her eighteen-month old daughter Nenaa’ikiizhikok to various islands on Lake of the Woods along the borders of Ontario and Minnesota to meet with her daughter’s father, Tobasonakwut, and see the Ojibwe pictographs in that area; she then visits “a special island on Rainy Lake that is home to thousands of rare books” (3). The juxtaposition of two parts in Erdrich’s journey helps tease out the conflation between books and islands covered in pictographs to show how these subjects of Erdrich’s study bear commonalities. Joining visual art and narrative, Books and Islands blurs the borders between the “stillness” of physical material objects, particularly sacred land and objects, and the temporal thrust of stories and histories, undoing the apparent incompatibility of “timeless” artefacts and the movement of history. I argue that Erdrich’s memoir uses ekphrasis as a way into reading the world as a text and perceiving texts as belonging to the material world, so that the islands and the islands’ pictographs, water, books, and people she encounters on Lake of the Woods and Rainy Lake consist of both stories and matter.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".