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Record W4321384435 · doi:10.1590/1983-80422022304566en

Motion picture resources and bioethics teaching in the human movement sciences

2022· article· en· W4321384435 on OpenAlexaff
Luciana Teixeira Waltrick, Fernando Hellmann, Gelcemar Oliveira Farias, Alcyane Marinho

Bibliographic record

VenueRevista Bioética · 2022
Typearticle
Languageen
FieldHealth Professions
TopicFilm in Education and Therapy
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsBioethicsMotion (physics)Exploratory researchPerceptionMovement (music)Human scienceContent analysisPsychologyPedagogySociologyMedical educationSocial scienceMedicineComputer sciencePolitical scienceArtificial intelligenceAestheticsArt

Abstract

fetched live from OpenAlex

Abstract This descriptive-exploratory study conducted with graduates from the Graduate Program in Human Movement Sciences at the Santa Catarina State University sought to understand the meanings of using movies in bioethics teaching and to identify motion pictures with bioethical themes related to exercise and health in the human movement sciences. Data were collected using semi-structured interviews and investigated by content analysis. A priori categories were based on the objectives. The participants’ answers generated the a posteriori subcategories, organized as follows: contributions to bioethics learning for the Graduate Program in Human Movement Sciences and professional life; and perceptions about using motion pictures as a pedagogical resource, including suggestions of movies and themes specific to the course. Movie resources with related themes make learning more meaningful and pleasurable, bringing the students’ professional realities closer to the bioethical content, facilitating such learning.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.824
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.126
GPT teacher head0.480
Teacher spread0.354 · 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 designTheoretical or conceptual
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
Published2022
Admission routes1
Has abstractyes

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