Bibliographic record
Abstract
In "That Snack Shame Story," I revisit a pivotal moment from my early teenage years, when I was placed on a strict diet that deeply impacted my relationship with food and my sense of agency. This graphic memoir uses illustrations and text to convey my experiences as a fat kid, highlighting the feelings of shame and secrecy that surrounded my hidden snack stash. Over four decades later, I can reflect on how this incident, among others, shaped my life and the ways it influenced my understanding of body image and self-worth. The narrative unfolds with a raw honesty, allowing readers to witness the emotional turmoil of being scrutinized for one’s body. As the story progresses, I explore the lasting effects of that moment, revealing how it galvanized my struggles with food and identity. In the final page, I offer a transformative perspective, crafting alternative endings that demonstrate the power of experience, knowledge, and community support. These “do-overs” present a vision of how the story could have unfolded differently, if only I knew then what I know now. By making this moment visible, I aim to loosen the grip of shame and foster connections with others who have faced similar challenges. "That Snack Shame Story" is not just a personal reflection; it serves as a call to reframe conversations about food, weight, and body image. It advocates for a compassionate dialogue that emphasizes love, independence, and understanding for fat kids everywhere. Through this comic, I hope to inspire a sense of solidarity and resilience, inviting readers to embrace their stories and redefine their relationships with food and self.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".