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Record W4379416993 · doi:10.1353/nib.2013.a525624

If You’re Not Part of the Solution . . .

2013· article· en· W4379416993 on OpenAlexaboutno aff
Sarah Giles

Bibliographic record

VenueNarrative Inquiry in Bioethics · 2013
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeDeportationGuard (computer science)PopulationPrisonPoliticsParliamentGeographyLawPolitical scienceHistoryCriminologySociologyImmigrationDemographyComputer science

Abstract

fetched live from OpenAlex

If You’re Not Part of the Solution . . . Sarah Giles I worked on an island that lured people to their deaths. I have come to realize that there are certain resources that every population must have in order to continue to exist. Health care providers are needed if a group is to continue to reside in one place. Without nurses and doctors, people tend to refuse to go to a location or choose not to stay. Perhaps if doctors like me refused to work in places where people have their basic human rights ignored, those places would cease to exist. It’s wishful thinking, but it’s the only way I can imagine the events of December 15th, 2010, could have been prevented. [End Page E11] Australia has a large offshore detention center on Christmas Island, a tiny dot in the middle of the Indian Ocean. Housed in a collection of prison–like facilities are men, women, and children who have come to an Australian Island claiming they are refugees. They have paid an average of $10,000 (U.S. dollars) to be smuggled by boat from nearby Indonesia and are then, usually, intercepted by the Australian navy or coast guard, and brought to the island to await processing. This processing can take two to four years and, even then, it can end with deportation. The asylum seekers spend years in a massively overcrowded and unstimulating purgatory. To say that I was not familiar with the political situation on the island would be a farcical under-statement. As a Canadian family doctor, I went to work on Christmas Island because of its exotic location and reputation for world–class SCUBA diving. It sounded like a dreamy place to do a locum. The little research I had done told me that there was a detention center on the island, but I had been told that I would only deal with those patients if they were sick enough to come into hospital. I didn’t imagine that I would end up feeling culpable for the deaths of 48 people who dreamed of restarting their lives in Australia. On December 15th, 2010, a rickety boat carrying approximately 100 asylum seekers somehow slipped past the navy and coast guard into the water just off of Christmas Island. The weather was foreboding and the waves were massive. As the boat approached the island, it lost power and, horrifically, smashed into the cliffs, leaving an estimated 48 men, women, and children to drown and an almost equal number in need of rescue and medical attention. My name was on the call schedule that day. All survivors were brought to the hospital. Our worlds collided. At the time, I thought it was good luck there was an extra doctor on the island that day. I was relieved that we were able to cope with the mass casualty situation and that only few survivors were sick or injured enough to require evacuation to the main land. Three years later, I wonder if the accident would have happened at all had we not been there. I had felt smug providing care in the public hospital, believing it was morally superior to working as a mercenary at the detention center. But I now realize that all of the health care providers had a role in allowing the tragedy to occur. What would have happened if all of the doctors refused to work with people held under those conditions? If that critical infrastructure of health care were not available on the island, the detention center could not be there. If the detention center weren’t there, the government would stop processing people there and perhaps desperate people would stop risking their lives to get to Christmas Island. Maybe the women and men who lost their children or the children who lost their parents would never have gotten on that boat. Maybe. Parts of that awful day stay with me. My job as a doctor prevented me from offering the compassionate care I wanted to provide and forced me to turn into a medical conveyor belt. When a young patient was laid in front of me, I assessed her, started...

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)0.002
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.409
Threshold uncertainty score0.843

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0120.003
Scholarly communication0.0080.014
Open science0.0030.009
Research integrity0.0140.020
Insufficient payload (model declined to judge)0.4090.205

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 head0.328
GPT teacher head0.496
Teacher spread0.167 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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
Published2013
Admission routes1
Has abstractyes

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