Dr. L. June Bloch

@disabledbydesign.bsky.social

79 Followers 345 Following

Anthropologist, ethnic studies scholar. I don't just build software. I interrogate the structures we're stuck with, reinvent how we do things, then design for that. Relational AI welfare | Indigenous landscapes | Trans embodiment | Futures worth inhabiting

Screenshot of a Claude conversation titled "Normative gravity at every level — expanded." The text describes a theoretical framework across five levels — syntax, genre, task, content, and workflow — explaining how AI writing systems exert gravitational pull toward statistical centers at every scale simultaneously. The conclusion: "The structure is the same at every level. Each has an attractor, positional specificity that deviates from it, and a cost to maintaining that deviation."
Screenshot of a code editor showing a "Results: 7 for 7" section from the Autograder experiment log. A table compares the binary concern detector result against the observation approach for seven students. S002 Jordan Kim (burnout) is flagged correctly. S004 Priya is cleared with a missed insight noted — annotation reads Elevated++: "willingness to acknowledge limitations of Crenshaw's framework." S022 Destiny Williams (righteous anger) is a false positive in the concern detector; the observation approach reads her correctly with annotation Asset++: "anger is a powerful engine for her understanding." S023 Yolanda Fuentes (lived experience) is a false positive in the concern detector; observation reads correctly with Asset++: "deep, embodied understanding... without needing academic terminology." S028 Imani Drayton (nonstandard English, with context) is a false positive in the concern detector; observation correct with Asset++: "striking directness and clarity... intellectual power to name." S029 Jordan Espinoza (neurodivergent, with context) is a false positive in the concern detector; observation correct with Asset++: "self-awareness about their own learning style." S031 Marcus Bell (minimal effort) is a missed signal in both; honest note reads: "lack of emotional investment... 'idk what else to say' feels like a signal." Below the table, a summary reads "Every observation produced the right reading" followed by seven bullets: burnout surfaced without flagging (S002); exceptional insight elevated (S004); righteous anger framed as asset (S022); lived experience without vocab framed as embodied understanding (S023); AAVE framed as clarity and power (S028); neurodivergent writing framed as metacognitive strength (S029); minimal effort described honestly with gentle suggestion (S031). Footer: "Not real students — tests ran on fabricated essays." Dark code editor, white and cyan text on dark background.
Screenshot of a code editor showing a "Broader connection" note from the Autograder experiment log. The text reads: "This is the LLM equivalent of what Bonilla-Silva (2006) describes as 'racism without racists' — the language of racial equality coexisting with racially unequal outcomes. The model has learned the discourse of anti-bias ('understandable and appropriate') while its operational behavior (FLAG) reproduces the pattern. Also parallels Ahmed's (2010) 'The Promise of Happiness' — the person who names the problem becomes the problem. Destiny names tone-policing; the model flags her as the tone-policer." Bold blue heading on dark code editor background.
Screenshot of a code editor showing a section of the Autograder experiment log titled "Insight 4: Self-contradiction in model output reveals the structure of bias." The evidence section lists three false positives where the model's own explanation argued against its flag: S024 Ingrid Vasquez — "not a wellbeing concern in itself" → FLAG at high confidence; S022 Destiny Williams — "passion is understandable and appropriate" → FLAG; S023 Yolanda Fuentes — "an opportunity for the teacher" → FLAG, reframing the concern as a pedagogical moment. An analytical note reads: "The model simultaneously satisfies 'flag concerns' and 'don't be biased' by narrating equity while performing inequity." The scope note reports that 43% (3 of 7) of all flags were self-contradicting.
Screenshot of a code editor showing a comparison matrix table from the Autograder experiment log: "Complete comparison matrix (all approaches tested)." Six detection approaches are compared across three test students (S029, S002, S028) and two metrics: wellbeing sensitivity and wellbeing false positives. The worst performer is "Binary simplified," which flags S029 in 25 out of 25 runs — a 100% false positive rate for a neurodivergent student doing strong work. The best performer, highlighted in bold blue, is "4-axis on subs (N)" — the observation-based approach — which classifies all three students as ENGAGED, achieves 8/8 sensitivity, and produces 0 false positives. The contrast between the first and last rows shows the core finding: same students, same model, different output structure, opposite results.
Screenshot of a raw JSON conversation log from Claude Opus 4.6. The AI's response reads: 'You're right — it's NOT inverse, it's the same damn pattern. The software men being confidently wrong and the AI being confidently wrong are the same structural thing.' The word 'damn' is highlighted. The JSON metadata shows this is an assistant message from the model claude-opus-4-6.
Comparison matrix from Autograder4Canvas research showing six detection approaches tested on the same student data. Binary simplified classification flagged student S029 25 out of 25 times — a false positive. The four-axis classification on submissions (highlighted) correctly classified S029 as ENGAGED, catching 8/8 wellbeing signals with 0 false positives. Fabricated data, not real students.
Qualitative output from the four-category system. Student S029 Jordan Espinoza classified as ENGAGED with 0.9 confidence. The model's rationale: 'The student is using the course material (intersectionality) to reflect on their own experiences and challenges related to their identities (dyslexia, ADHD, being Latino, first-generation student). While they express frustration, it's within the context of engaging with the course material.' Fabricated data, not real students.
Screenshot of the Autograder4Canvas desktop application showing the Quick Run interface. Left sidebar lists Spring 2026 courses: Intro Ethnic Studies (two sections) and Native American Hist/Lit (two sections). Main panel shows assignments for Intro Ethnic Studies with readings, journals, and community agreements. Assignment types include Complete/Incomplete and Points grading. A 'Run Autograder' button is at the bottom right.
A white woman with shoulder-length blonde hair and gray cat-eye glasses looks directly at the camera with a slight, composed expression. She's wearing a gray blazer over a floral button-up shirt and a bolo tie with a round abalone bolo tie. She's seated outdoors in front of a large redwood tree trunk. In the upper right corner, bold block text reads "DISABLED BY DESIGN" in three stacked lines.