Women's voices in Old Norse-Icelandic poetry
Bibliographic record
Abstract
This thesis examines the multiplicity of ways in which Old Norse-Icelandic authors represented women’s voices in poetic form, in a wide variety of genres. It undertakes a detailed analysis of a selection of these verses, considering how they function as poetry, how they function in relation to their prose context, and the (literary) effect of the prose author’s attribution of the verses to a woman. It considers the many and varied ways in which poets and saga authors represent the female poetic voice, including ways in which this voice may be used to evoke a mythological past, create a sense of nostalgia, destabilize or support established gender norms, function as performative speech, structure an episode or a longer poem, or allow an author/compiler to engage with contemporary societal tensions and concerns. It is not a comprehensive survey of all existing examples of women’s voices within Old Norse-Icelandic poetry, but examines a range of (largely secular) verses along a continuum from the entirely fictional to the ostensibly historical. The introduction provides a survey of existing scholarship, including relevant scholarship in women’s studies and gender studies, and problematizes the frequent separation of eddic-style and skaldic-style verses in past analyses. Chapter 1 examines supernatural and legendary women’s voices, and draws parallels between the structural effects of verses within prosimetric texts, and the structural effects of speech acts within longer poetic works. Chapter 2 analyzes women’s dream verses, particularly in Sturlunga saga, and the effects of women’s roles as both speakers and dreamers of verse. Chapter 3 briefly charts the representation of women’s relationship with literary culture in indigenous romance, and considers the fusion of skaldic verse and chivalric tradition in the verses spoken by women in Víglundar saga. Chapter 4 turns to the women of the Íslendingasögur and konungasögur, including both the traditionally recognized skáldkonur as well as many other women whose verses have regularly been subject to questions surrounding attribution. The chapter conducts a detailed analysis of the many ways such verses function within the sagas. Throughout, this thesis reveals the extent to which careful arrangement of verses allows an author or compiler to highlight relationships between characters, argues for the (intentional) effectiveness of women’s verses as structural markers, and demonstrates that women’s roles, and women’s engagement with poetic composition – far from being a monolithic or fixed state of affairs – are a site of tension, conversation, and ongoing debate throughout these texts.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".