Reclaiming and Renegotiating Authenticity Through Autofiction: Meena Kandasamy’s When I Hit You
Bibliographic record
Abstract
Autofiction, often regarded as an innovative means of self-exploration and self-presentation, invites discussions of authenticity. Highlighting the complexity and social value of the notion, I suggest that authenticity is not an outdated ideal that autofiction seeks to transcend; rather, autofiction opens up ways to critically engage with this notion. This potential is realized in Meena Kandasamy’s When I Hit You, a work of “biographical autofiction” that proclaims to be “fiction” but does not contain any perceivable elements of invention. Critiquing Genette’s dismissal of biographical autofiction as “veiled autobiography,” I argue that the paratextual label of “fiction” is not a gesture of evasion but a liberating leap that makes space for the author to renegotiate authenticity, a notion that is highly at stake in the narration of domestic violence but systematically denied to female survivors. A close analysis of the work informed by this new metaphor shows that Kandasamy negotiates four forms of authenticity with her autofictional performances: a partial authenticity that recognizes female survivors’ need for self-protection, an emotional authenticity that registers the psychological repercussions of domestic violence, an emergent authenticity that gives women the space to heal and grow, and a collective authenticity that highlights the importance of culturally sanctioned narrative templates. Kandasamy’s work highlights the need to continually scrutinize and renew our ideas of authenticity and shows the constructive role autofiction can play in this process.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.024 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".