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Engineering and Environmental Technoethics

2010· book-chapter· en· W4255377121 on OpenAlexaff
Rocci Luppicini

Bibliographic record

VenueAdvances in information security, privacy, and ethics book series · 2010
Typebook-chapter
Languageen
FieldArts and Humanities
TopicWar, Ethics, and Justification
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsNature versus nurtureEngineering ethicsEngineeringEnvironmental technologyPublic interestEquity (law)Political scienceSociologyLaw

Abstract

fetched live from OpenAlex

This chapter traces the development of Engineering Ethics, Computer Ethics, and Environmental Technoethics. It also covers the topic of military technoethics as an important new development that deserves special attention. The story begins in the late 19th century with the development of various engineering professional bodies to ensure that engineers were responsible for potentially harmful constructions. This in turn, gave rise to the creation of codes of engineering ethics to help guide professional conduct. As the public demand for engineering increased throughout the 20th century, so did the ethical implications and demand for codes of engineering ethics. In the 1950s and 1960s, the continued expansion of industrial growth lead also to a number of human caused environmental disasters ranging from oil spills to nuclear explosions to the release of toxic chemicals into the air and water supply. This brought on a public reaction among environmental organizations and increased public attention to ethical implications of technology and the environment. These developments helped nurture in studies in environmental technoethics and the ethical concern over human involvement in technology related environmental change. Also in the 1950s and 1960s, the public use of mainframe computers, promising outlook for computer networking, and scholarly interest in systems research raised additional interest concerning the ethical implications connected to computer innovation in society. This chapter provides a review of background developments, challenges, and current directions in each of these areas. It uses examples to illustrate the potency of technology in reference to key areas (i.e., access equity, software design, computer navigation systems, construction, mining, and other areas of technology use and misuse). It concludes with insider interviews from leading experts working in the field and recommendations on how to use technoethical inquiry to leverage the ethical use of science and technology in areas where technological innovation has created ethical challenges and dilemmas.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.826
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.006
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.234
Teacher spread0.214 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2010
Admission routes1
Has abstractyes

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