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Critically Conscious Engineers: Reforming Humanitarian Engineering for Gender Equality in Education

2022· article· en· W4313562693 on OpenAlexaff
Ruby G. Kantharajah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicEducation, Technology, and Ethics
Canadian institutionsLakehead University
Fundersnot available
KeywordsEngineering ethicsConsciousnessScope (computer science)Engineering educationCritical consciousnessSociologyCritical theoryPolitical scienceEngineeringPedagogyEngineering managementPsychologyComputer scienceLaw

Abstract

fetched live from OpenAlex

This paper presents a conceptual understanding of critical consciousness theory and its application in engineering. More specifically, it provides a deeper inquiry into the integration of critical consciousness in engineering pedagogy and practice such as humanitarian engineering. A brief analysis of the critical consciousness construct – critical reflection and critical action as applied in engineering education provides a scope into how developing a sociopolitical lens could shape the design and development of technologies for achieving gender equality in education in vulnerable communities, particularly developing nations afflicted by crises – natural disasters, armed conflict, and other violence. Examples draw from studies in education, culturally responsive school leadership, and engineering education. The purpose of this exploration is to present an overview of how cultivating critical consciousness in engineers and engineering educators, professionals, and leaders can be an entry point into achieving the United Nations Sustainable Development Goals to empower girls and protect their rights to access education.

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)0.012
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0070.040
Scholarly communication0.0090.012
Open science0.0010.012
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0040.001

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 head0.033
GPT teacher head0.303
Teacher spread0.270 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2022
Admission routes1
Has abstractyes

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