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Record W1956251346 · doi:10.1109/icl.2015.7318115

From the fundamentals to the Praxis: Constructing a different engineering education to make our world a less risky place

2015· article· en· W1956251346 on OpenAlexaff
Cristiano Cordeiro Cruz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicCognitive Science and Education Research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPraxisSet (abstract data type)CollateralPsychological resilienceEngineering ethicsCollateral damageComputer scienceRisk analysis (engineering)SociologyPolitical scienceEngineeringPsychologyBusinessLawSocial psychology

Abstract

fetched live from OpenAlex

If we look to the human history we will see that, along with several marvelous achievements, technical development has given many bad fruits, either as intended outcomes (e.g., bombs and weapons) or as foreseeable or unpredictable side effects (e.g., environmental destruction and social disruption). This same history can also unveil a technology that is much less neutral, always embodying social values and, as a consequence of the chosen set of them, dealing very differently with the collateral effects, the possibility of their occurrence and their prevention. According to such understanding, many claims for resilience may be questioned when the calamity being experienced is caused by technology. Actually, in such case, the more sensible thing left to be done seem to be trying to change technique or the pattern of its development, instead of only adapting to or bearing the suffering it causes/d. Since the professional eventually in charge of technical design and implementation is the engineer, we must thus think about the formation we currently provide him/her. For, depending on the skills we are allowing or encouraging them to develop, the threat of a more risky world may be bigger or smaller. In this paper, besides undertaking and substantiating this reflection, we will also present a possible model for such an education to be achieved. It is important to highlight, however, that our approach will be a more philosophical one, based on some important authors of this area and focusing on the fundamentals of what will be discussed (e.g. technology and technical development). Moreover, our main intention is not to offer our readers a definitive and universal answer, but rather to leave them (you) with questions and the desire to search and try different solutions that could finally give the fruits we most need currently: engineers able to perform a social sensitive technical job.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.126
GPT teacher head0.381
Teacher spread0.255 · 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 designQualitative
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
Published2015
Admission routes1
Has abstractyes

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