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Record W4412870714 · doi:10.24908/pceea.2025.19632

Student Perceptions of "Tech Stewardship"

2025· article· en· W4412870714 on OpenAlexafffundvenueabout
Kari Zacharias, Jillian Seniuk Cicek, Paula Rodrigues Affonso Alves, Fenella Amarasinghe, Jeffrey Harris, Renato Rodrigues, Alexi Orchard, Rosheedat Adeniji

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsMemorial University of NewfoundlandYork UniversityUniversity of Manitoba
FundersMemorial University of NewfoundlandUniversity of WaterlooYork UniversitySuncor Energy Incorporated
KeywordsStewardship (theology)PerceptionBusinessEnvironmental resource managementPsychologyEnvironmental planningPolitical scienceGeographyEnvironmental science

Abstract

fetched live from OpenAlex

Technological stewardship, or tech stewardship, is an approach to guiding the design, implementation, or use of technologies. Within Canada, the most prominent use of this term is the Tech Stewardship Practice Program (TSPP), an online course developed by the Engineering Change Lab. While the TSPP has reached thousands of people since its inception in 2022, the term tech stewardship has not yet been widely adopted within engineering education or industry. This study is based in data from focus groups conducted with undergraduate engineering students participating in the TSPP, and characterizes students’ perceptions of “tech stewardship.” We find that students came to the TSPP with disparate understandings of what stewardship means, and developed different understandings as they moved through the program. Student perceptions across and within focus groups demonstrated a range of interpretations of and relations to the TSPP, and pose a variety of questions for educators who may seek to engage with tech stewardship in their teaching practice.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.015
GPT teacher head0.351
Teacher spread0.335 · 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 designObservational
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
Published2025
Admission routes4
Has abstractyes

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