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Record W2263659320

Ethics of Experimenting With People's Lives in Winter Backcountry

2010· article· en· W2263659320 on OpenAlexaboutno aff
Bob Uttl, Dylan Smibert, Alain Morin, Gregory M. Wells, Jan Uttl, Breanne Hamper

Bibliographic record

Venue2010 International Snow Science Workshop · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsnot available
Fundersnot available
KeywordsDamagesAccident (philosophy)PsychologyRecallHistoryPolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

The purpose of the Avaluator Avalanche Accident Prevention Card, designed by Haegeli and McCammon (2006) and published by the Canadian Avalanche Association (CAA), was to reduce the number of avalanche accidents in Canada. Speaking to the ISSW 2006 audience, McCammon (October 3, 2006) announced an experiment on the Avaluator's effectiveness: “This is an experiment. This is an experiment with people's lives, with their loved ones.” Subsequently, we (Uttl et al., 2008a,b; 2009a,b,c,d) have shown that (1) the data behind the Avaluator are not available for inspection, (2) Haegeli and McCammon inappropriately excluded 1,148 avalanche records (82% of their sample) due to missing data, (3) the Obvious Clues prevention values in the Avaluator are grossly inflated, and (4) the number of accidents in Canada doubled following the introduction of the Avaluator. The two new disclaimers in the latest printing of the Avaluator (2009) advise that the Avaluator's Obvious Clues Method is not suitable for “any particular purpose” and that the Canadian Avalanche Center (CAC) is not responsible for any “injuries or death” or other damages caused by the Avaluator. Inexplicably, the CAA and CAC continue to claim that the Avaluator is “the best tool” and have not recalled it. We asked over 100 individuals how ethical various actions taken by the developers, CAA, and CAC (e.g., not recalling it) are. The participants rated the actions as nearly extremely unethical and believed that the developers, CAA and CAC should “tell the truth” and recall the Avaluator.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.343
Teacher spread0.323 · 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 designObservational
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

Citations1
Published2010
Admission routes1
Has abstractyes

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