MétaCan
Menu
Back to cohort
Record W1490198769

Biotechnology literacy: Assessing the knowledge and attitudes of student teachers

2013· article· en· W1490198769 on OpenAlexaboutno aff
John Barnett, Carol Ann Lane

Bibliographic record

VenueScholarship@Western (Western University) · 2013
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsnot available
Fundersnot available
KeywordsLiteracyMathematics educationPedagogyScientific literacyPsychologyMedical educationScience educationMedicine
DOInot available

Abstract

fetched live from OpenAlex

In modern society, science and technology have become key fields affecting the daily lives of all citizens. Furthermore, young people will need this new knowledge in their future careers and in their daily lives as members of a technologically-imbued society. Developing a level of biotechnology literacy across Canadian society may become an essential factor in future prosperity. Teachers need a high level of this literacy to foster students’ development of this competency.\nThis paper is based on preliminary results from an international survey in Canada, China, and Spain. The survey aims to better understand the knowledge and attitudes of student teachers towards biotechnology which could potentially reflect on their teaching in the future; in other words their level of biotechnology literacy. The validated survey was administered to intermediate/senior and primary/junior pre-service students in the Faculty of Education. Canadian survey results for the primary/junior pre-service students are compared with those in the intermediate/senior program. These results will be compared to those obtained in other countries.\nSince biotechnology is having an ever increasing impact on society, this research will provide insight into the extent pre-service education programs need to be expanded and re-developed in order to incorporate and address the growing need for such knowledge.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.014
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.048
GPT teacher head0.324
Teacher spread0.276 · 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
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueScholarship@Western (Western University)Same topicBiomedical and Engineering EducationFrench-language works237,207