MétaCan
Menu
Back to cohort
Record W7053073091

Teachers’ storytelling production for teaching science with sensitivity to cultural diversity

2023· article· en· W7053073091 on OpenAlexfundno aff

Bibliographic record

VenuePortuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2023
Typearticle
Languageen
FieldEngineering
TopicMagneto-Optical Properties and Applications
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaUniversidade do MinhoInternational Council for Canadian Studies
KeywordsStorytellingDiversity (politics)Science educationKnowledge productionCultural diversitySociology of scientific knowledgeCultural knowledgeCultural sensitivity
DOInot available

Abstract

fetched live from OpenAlex

This paper presents the results of a research study that analysed how future science teachers’ production of short stories can contribute to their training to be sensitive to the cultural diversity of classrooms. The methodology was qualitative, and data collection was carried out through a workshop on short story production with the participation of future teachers who attended the Basic Education undergraduate course at the Polytechnic Institute of Guarda, Portugal, with subsequent documentary and descriptive analysis. The results reveal that the participants were able to organise different cultural knowledge about nature to build their short stories. Furthermore, epistemological and ontological differences could be related to school scientific knowledge. These relations and their implications for intercultural dialogue in science teaching are pointed out. This study showed that the construction of short stories helps future teachers to investigate and reflect in advance on opportunities that science teaching can provide for intercultural dialogue in classrooms when scientific knowledge is related to other forms of 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 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)0.008
metaresearch head score (Gemma)0.028
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 score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.028
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0050.005
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.070
GPT teacher head0.348
Teacher spread0.279 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venuePortuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)Same topicMagneto-Optical Properties and ApplicationsFrench-language works237,207