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Record W7004564195

The mad manifesto

2023· dissertation· en· W7004564195 on OpenAlexaff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2023
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicBryophyte Studies and Records
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsManifestoMandateRhetorical questionUniversal designUniversal Design for LearningAction (physics)DualismWork (physics)
DOInot available

Abstract

fetched live from OpenAlex

The “mad manifesto” project is a multidisciplinary mediated investigation into the circumstances by which mad (mentally ill, neurodivergent) or disabled (disclosed, undisclosed) students faced far more precarious circumstances with inadequate support models while attending North American universities during the pandemic teaching era (2020-2023).
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\nUsing a combination of “emergency remote teaching” archival materials such as national student datasets, universal design for learning (UDL) training models, digital classroom teaching experiments, university budgetary releases, educational technology coursewares, and lived experience expertise, this dissertation carefully retells the story of “accessibility” as it transpired in disabling classroom containers trapped within intentionally underprepared crisis superstructures. Using rhetorical models derived from critical disability studies, mad studies, social work practice, and health humanities, it then suggests radically collaborative UDL teaching practices that may better pre-empt the dynamic needs of dis/abled students whose needs remain direly underserviced. 
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\nThe manifesto leaves the reader with discrete calls to action that foster more critical performances of intersectionally inclusive UDL classrooms for North American mad students, which it calls “mad-positive” facilitation techniques:
\n1.\tSeek to untie the bond that regards the digital divide and access as synonyms.
\n2.\tUDL practice requires an environment shift that prioritizes change potential.
\n3.\tAdvocate against the usage of UDL as a for-all keystone of accessibility.
\n4.\tRefuse or reduce the use of technologies whose primary mandate is dataveillance.
\n5.\tRemind students and allies that university space is a non-neutral affective container.
\n6.\tOperationalize the tracking of student suicides on your home campus.
\n7.\tSeek out physical & affectual ways that your campus is harming social capital potential.
\n8.\tRevise policies and practices that are ability-adjacent imaginings of access.
\n9.\tEliminate sanist and neuroscientific languaging from how you speak about students.
\n10.\tVigilantly interrogate how “normal” and “belong” are socially constructed.
\n11.\tTreat lived experience expertise as a gift, not a resource to mine and to spend.
\n12.\tCreate non-psychiatric routes of receiving accommodation requests in your classroom.
\n13.\tSeek out uncomfortable stories of mad exclusion and consider carceral logic’s role in it.
\n14.\tCenter madness in inclusive methodologies designed to explicitly resist carceral logics.
\n15.\tCreate counteraffectual classrooms that anticipate and interrupt kairotic spatial power.
\n16.\tStrive to refuse comfort and immediate intelligibility as mandatory classroom presences.
\n17.\tCreate pathways that empower cozy space understandings of classroom practice.
\n18.\tVector students wherever possible as dynamic ability constellations in assessment.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.978
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.182
Teacher spread0.171 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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