Malingerer or Maligned: A Comparative Study of Multiple Chemical Sensitivity Case Law
Bibliographic record
Abstract
People with invisible illnesses are challenged both by their impairments and the additional hurdles they face when it comes to being believed by others. This presents particular difficulties when seeking to convince those with the authority to make legal findings of discrimination or entitlement to accommodation. By focusing on the historically contested diagnosis of multiple chemical sensitivities (“MCS”), this paper examines the outer limits of how disability is defined and the legal rights given to people whose claims are viewed as suspect. Specifically, comparison is made between MCS employment cases in the United States and Canada.In both jurisdictions, plaintiffs face challenges related to findings of credibility, a prioritization of scientific evidence over experiential knowledge, and efforts to rule out accommodation measures by exaggerating disabilities. In the end result, people with MCS are statistically more likely to succeed in Canada; but, the tide is turning. Twenty-five years after the passage of the Americans with Disabilities Act, American courts are becoming more receptive to a plaintiff’s lived experience of MCS as a disability. At the same time, Canadian tribunals are increasingly looking to scientific and medical validation at the expense of a claimant’s lived reality.As new diseases emerge, the law in both countries must progress to allow medical and scientific evidence to follow, not lead, experiential accounts of disablement in the workplace. Otherwise, the result will be to create a class of people with invisible disabilities who have no hope of being rendered visible by our courts and tribunals.
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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 | 0.023 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
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".