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Record W1988642264 · doi:10.1177/07417130122087241

Ethical Issues and Codes of Ethics: Views of Adult Education Practitioners in Canada and the United States

2001· article· en· W1988642264 on OpenAlexaffabout
Wanda Marja Gordon, Thomas J. Sork

Bibliographic record

VenueAdult Education Quarterly · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsUniversity of British ColumbiaUniversity of the Fraser Valley
Fundersnot available
KeywordsEthical codeEmpirical researchEthical issuesEngineering ethicsInformation ethicsResearch ethicsSociologyMedical educationPublic relationsPolitical scienceLawMedicine

Abstract

fetched live from OpenAlex

Although the ethics of practice has become increasingly visible in the adult education literature over the past two decades, little empirical research has been done to inform the dialogue and debate. The purpose of this study was to examine the views of adult education practitioners in British Columbia about the need for a code of ethics and about the ethical issues, concerns, and dilemmas experienced in their practice. The study was an approximate replication of research carried out in Indiana reported by McDonald and Wood. This study was undertaken to broaden the empirical database within adult education, provide further insight into the ethics of practice, and determine similarities and differences between Canadian and American adult educators in their encounters with ethical issues and their views about codes of ethics. Major findings confirm positive practitioner views about codes of ethics and are generally consistent with the findings reported by McDonald and Wood.

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)0.013
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.905
Threshold uncertainty score0.692

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.032
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0350.018
Scholarly communication0.0090.002
Open science0.0020.006
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.347
Teacher spread0.331 · 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 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

Citations39
Published2001
Admission routes2
Has abstractyes

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