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Record W118308585

Values, ethics and empowering the self through cooperative education

2011· article· en· W118308585 on OpenAlexfundno aff
Matthew Campbell, Karsten E. Zegwaard

Bibliographic record

VenueGriffith Research Online (Griffith University, Queensland, Australia) · 2011
Typearticle
Languageen
FieldDecision Sciences
TopicEthics in Business and Education
Canadian institutionsnot available
FundersUniversity of South AfricaTshwane University of TechnologyUniversity of SurreyUniversity of WaterlooFlinders UniversityUniversity of New EnglandMurdoch UniversityMassey UniversityUniversity of JohannesburgCentral Queensland UniversityAuckland University of Technology, New ZealandAustralian Catholic UniversityUniversity of Western SydneyUniversity of Waikato
KeywordsEngineering ethicsArgument (complex analysis)Professional ethicsApplied ethicsProfessional developmentInformation ethicsPublic relationsPolitical scienceLegal ethicsSociologyPedagogyMedicineEngineering
DOInot available

Abstract

fetched live from OpenAlex

Following the recent global financial crisis and the collapse of major organisations such as Lehman Brothers, and the earlier corporate failings of Enron and HIH, there has been a shift of focus towards the role of ethics education in the formation of business professionals. In other professional settings, such as policing and medicine, similar major crises have highlighted the significance of the early development of ethical practice in emerging professionals. This paper considers the nature of professional ethics for an emerging professional, arguing that professional ethics should be a key factor in cooperative education programs. The paper considers the role of values and ethics education in empowering the emerging professional to shape and change their workplace. Building on this argument, the paper suggests foundational elements of an approach to professional ethics in cooperative education programs concluding with a suggested research path for further exploration of the content and nature of such an approach.

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)0.010
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.004
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 head0.659
GPT teacher head0.535
Teacher spread0.125 · 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 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

Citations27
Published2011
Admission routes1
Has abstractyes

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