Unmasking Motherhood: Journeys of Self-Discovery in Mary Melfi’s Italy Revisited: Conversations with My Mother and Genni Gunn’s Tracing Iris
Bibliographic record
Abstract
This paper will explore how the quest for the mother (whether the missing one of Gunn’s novel or the living one of Melfi’s memoir) leads the protagonists of both books to rediscover their mother tongue, that is a language based on communication and community building (Parmod, 2008), or as Kate defines it in Tracing Iris, a body language. It is through this language that both characters are able to challenge the patriarchal concept of motherhood and, in particular, the stereotype of the good versus the bad mother. The quest for the mother in both texts can be defined as a journey through the protagonists’ past that leads them also to problematize the notion of motherland. Significantly, both texts emphasize the idea of resurrection (the resurrection of the self) and their structures seem to mirror the trajectory identified by Podnieks and O’Reilly (2010) as representing the transition from daughter-centric to matrifocal narratives. Such narratives are effective tools in unmasking motherhood not only because they enable mothers to have a voice but also because they represent a different kind of mothering, the mothering of writing (Nayar 2008, p. 140). According to Nayar, textual mothering, by giving birth to stories and narratives, allows motherhood to be reinterpreted as a situation of power and identity (Nayar 2008, p. 140).
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.037 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".