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Record W3096379237 · doi:10.1007/978-3-030-27874-8_19

Technological Fixes and Antimicrobial Resistance

2020· book-chapter· en· W3096379237 on OpenAlexaff
Nicholas B. King

Bibliographic record

VenuePublic health ethics analysis · 2020
Typebook-chapter
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsAppealUnintended consequencesPoliticsTechnological changeFaithPsychological interventionResistance (ecology)Simple (philosophy)Political scienceLaw and economicsEnvironmental ethicsRisk analysis (engineering)EconomicsBusinessEpistemologyLawMedicinePhilosophyEcologyBiology

Abstract

fetched live from OpenAlex

Abstract A ‘technological fix’ reduces the negative impact of a problem without addressing its underlying political, economic, or social causes. This chapter examines antimicrobials’ central role in both the modern faith in technological fixes in medicine, and critiques of over-reliance on technological interventions that produce unintended consequences. The enduring appeal of technological fixes is rooted in their promise to provide simple, efficient, measurable, and effective solutions to complex problems; but this practically is purchased at the price of eliding important distributive concerns.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.217
GPT teacher head0.397
Teacher spread0.179 · 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.

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

Citations1
Published2020
Admission routes1
Has abstractyes

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