ENGAGING WITH MAL(E)FUNCTIONS: GENDER AND ENVIRONMENTAL CRISIS IN DAPHNE MARLATT’S ANA HISTORIC
Bibliographic record
Abstract
Daphne Marlatt's novel Ana Historic presents a female-oriented version of historical events not based on male-centered modes of representation. Marlatt fictionalizes aspects of historical and literary documents to carve out a space for an imagined female history that counters masculine production- based narratives previouslywritten about the logging camps of British Columbia. Ana Historic provides a concise look at previous representations of resources and women, which offers insight into how these issues inform a contemporary viewpoint regarding natural resources. Moreover, if the environmental crisis we are experiencing globally is a result of industrialization, have women been manufactured in a similar fashion? And if so, how can North Americans attempt to counter environmental crisis from a societal perspective that is heavily implicated in upholding patriarchal structures? The answer extends beyond mere syllogism, and into the complicated realm of capital: both monetary and social. Revisiting Ana Historic in our current moment of environmental uncertainty reflects the interconnected relationship between so-called natural spaces and engendered representation. This causes an identity crisis for landscapes and gender, leading to the manipulation of these spaces by established patriarchal institutions and industrializing structures. This represents an extension beyond the consideration of women as "Mother Nature" and relates specifically to their manipulation by men into resources that furthermale capital. Marlatt's transformation of the forest into a manufactured product mimics her transformation of the female protagonists into appropriately functioning wives and mothers. Marlatt's main protagonists Ana, Ina, Annie resist manufacture and become the monsters of male-functions. The transformation of landscapes and women involves looking at the forest as represented in the natural environment, the sawmill, and the finished product. The malfunction of the proper male functioning in the novel is what I come to label as mal(e)functions. The term mal(e)functions, while embodying the simultaneity of two words also represents the non male-oriented spaces in the novel; that of the forest and the female protagonists. The split in the term also represents the fissures of male centered narratives where women and environmental consciousness can exist.
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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 | 0.000 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".