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Record W3124799479

The Identifiability Bias in Environmental Law

2007· article· en· W3124799479 on OpenAlexaboutno aff
Shi-Ling Hsu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLegal and Constitutional Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIdentifiabilityLawmakingLaw and economicsDoctrineEnvironmental lawCommon lawPolitical scienceLawPublic economicsEconomicsLegislature
DOInot available

Abstract

fetched live from OpenAlex

THE IDENTIFIABILITY BIAS 435Herceptin would become fundable by the province within a few months. 6Health Minister George Smitherman was quoted in the article as saying "I'm a human being like anybody else and I'm personally impacted by personal stories . . . .I have a very, very keen personal sense of the degree to which this is a tremendously impactful decision point for some women and many families in the province of Ontario." 7 It certainly seems callous to take issue with the Health Minister's intervention.But, one wonders, when George Smitherman jumped Herceptin to the top of the queue, what drugs were pushed aside?Might there have been people who were hurt by this move?One drug that might have been held up was Methotrexate, which was approved for provincial funding in October 2005, shortly after Herceptin's approval. 8Methotrexate is a curiously versatile drug that is used in chemotherapy treatment for acute lymphoblastic leukemia (most commonly children's leukemia), rheumatoid arthritis, Crohn's disease, psoriasis, and ironically, breast cancer. 9Much is still unknown about Methotrexate, despite a great deal of clinical study. 10Was a possible delay in the funding of Methotrexate worth it?Quite possibly, but a rational inquiry would examine a variety of factors, including the incidences of the diseases treated by Methotrexate and Herceptin, their effectiveness in treating such diseases, their incremental effectiveness relative to existing treatments, and perhaps their cost.The week that Herceptin was approved, the Canadian Medical Association Journal published an editorial questioning the wisdom of the province's approval, especially given the $148 million price tag funding Herceptin and two other drugs over a three-year period. 11ut the way that a newspaper story changed, in one day, the priorities of the largest provincial health ministry in Canada is much more important and subtle.The response to the June 23, 2005 article, which focused so intensely on Leslie Cowan and her family, 12.Although they do not set out a formal definition, Deborah A. Small & George Loewenstein describe and study the phenomenon in more specificity in their article The Devil You Know: The Effects of Identifiability on Punishment.18

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 imitation

Not 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.

metaresearch head score (Codex)0.076
metaresearch head score (Gemma)0.191
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.076
Threshold uncertainty score0.404

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0760.191
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0060.041
Scholarly communication0.0100.021
Open science0.0030.006
Research integrity0.0120.015
Insufficient payload (model declined to judge)0.0190.002

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 head0.044
GPT teacher head0.212
Teacher spread0.168 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations7
Published2007
Admission routes1
Has abstractyes

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