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The development of an instrument to assess science students' views on the social responsibility of scientists

2010· article· en· W970868032 on OpenAlexaboutno aff
Dürten Röhm, Marissa Rollnick

Bibliographic record

VenueAfrican Journal of Research in Mathematics Science and Technology Education · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsScience educationNature of ScienceProcess (computing)Social responsibilityScientific instrumentSocial science educationScience, technology, society and environment educationSociologyMathematics educationEngineering ethicsPsychologyPedagogyPublic relationsPolitical scienceEngineeringComputer science

Abstract

fetched live from OpenAlex

This paper reports on the development of an instrument which was used to gather information on the views of science students on the social responsibility of scientists. The methodology is based on a “Views on Science-Technology-Society (VOSTS)” instrument which was developed in Canada. It involves three phases, employing interviews and free response and fixed response questionnaires, respectively. Qualitative data analysis at each stage of the development of the instrument provided the input for the following stage. Participants were drawn over a two year period mainly from chemistry students at various levels of academic study at the University of South Africa. Procedures are described separately for each of the three phases in the development of the instrument and are elucidated by actual findings abstracted during the development process. The final questionnaire statements cover topics in the fields of education, science, technology and society, the scientist as individual and the scientific enterprise. They include statements on scientific freedom, the technological imperative, science in Africa, women in science, whistleblowing, education of the public and the role of science students in society.

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)0.084
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0840.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.008
Science and technology studies0.0030.014
Scholarly communication0.0000.000
Open science0.0030.000
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 head0.258
GPT teacher head0.570
Teacher spread0.312 · 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; both teacher heads agree on what is shown here.

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
Published2010
Admission routes1
Has abstractyes

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