Gendered and Racialised Ambiguity: An Exploration of Métisses Women’s Complex Identities and Representations in French-African contexts
Bibliographic record
Abstract
The term métis a French word referring to a person of mixed ancestry - which, for the \npurposes of this research, will designate a person specifically of ‘black’ and ‘white’ parentage \n- despite its absence of biological reality, has been used increasingly in the present \nfrancophone society where the possibilities of contact between peoples and cultures have \nmultiplied. Representing both an individual and collective experience, it is a complex \nconcept, reflecting a pluralistic and moving reality that not only points out the relationship of \neach individual to their own history and identity, but also the relations that society maintains \nwith otherness. Whether assumed, rejected, or subject to indifference, the métis identity is \nthus a major space for reflection on the evolution of today’s multicultural societies. \nIn this context of a moving and evermore dynamic notion of identity, the figure of the \nmétisse woman in particular (whether in literature or society) stands at the crossroads of \ndiscourses on race and gender, as well as class and sexuality. The present research therefore \naims at offering a non-exhaustive overview of feminine afro-descendant métissage by \nstudying the processes of identity construction, evolution, and representation of this \nambiguous figure, with a particular focus on in-betweenness, double consciousness, and \nsense of belonging. To do so, the study will first rely on literary works from the past \ncenturies, from canons such as Charles Baudelaire’s Les Fleurs du Mal (1857) and Emile Zola’s \nThérèse Raquin (1867) to Abdoulaye Sadji’s Nini, mulâtresse du Sénégal (1951) portraying the \nmétisse through the writers’ (male) gaze upon her. Secondly, this study will propose an \nanalysis revolving around the lived experiences of five women of French-African descent \nusing semi-structured interviews. The participants, drawing on their own personal \nunderstanding of their identity, were invited to share aspects of their stories, to contrast and \nassess traditional representations of métisses women. In light of the literature representing \nthe métisse and of the interviews’ findings giving her voice, the present research seeks to \ninterrogate what it means to be a métisse woman and explore how her ambivalent identity is \nimagined, constructed and experienced as fundamentally intersectional, gendered and \nracialised.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.018 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".