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Record W2524041302

Head Starts and Extra Time: Academic Accommodation on Post-Secondary Exams and Assignments for Cognitive and Mental Disabilities

2016· article· en· W2524041302 on OpenAlexaff
Bruce Pardy

Bibliographic record

VenueSSRN Electronic Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsQueen's University
Fundersnot available
KeywordsAccommodationAffect (linguistics)CognitionCognitive disabilitiesPsychologyTest (biology)Reasonable accommodationCompetition (biology)Cognitive skillApplied psychologyMathematics educationMedical educationPolitical scienceMedicinePsychiatryLaw
DOInot available

Abstract

fetched live from OpenAlex

Universities and colleges routinely grant extra time on exams and assignments to accommodate students with cognitive and mental disabilities. Such accommodation is inappropriate and inconsistent with the law. Exams and assignments are, in part, competitions. Like a head start in a race, extra time means that the competition is no longer valid. Races test speed, and no accommodation can be made for disabilities that affect speed, which is the bona fide criterion of the race. Exams and assignments assess a range of cognitive and mental skills, and no accommodation can be made for disabilities that affect those skills, which are bona fide criteria of the assessment. A head start imposes undue hardship on other runners, and extra time imposes undue hardship on other students in the class. The purpose of accommodation is to facilitate participation, not to compensate for lack of ability that is relevant to the test. Students with mental disabilities are able to sit exams without extra time, which means that they are already able to participate. The real purpose of claims for extra time is to increase their prospects for success at the expense of other students, which is not legitimate. Universities and colleges should not provide extra time as an accommodation for disabilities that relate to cognitive and mental skills.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.852
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.029
GPT teacher head0.348
Teacher spread0.319 · 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 designOther design
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

Citations2
Published2016
Admission routes1
Has abstractyes

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