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Physics Education from a decolonial perspective: a case study with Brazilian curriculum

2024· article· en· W4393192078 on OpenAlexaff
Carlos Mometti, Tanja Tajmel, Maurício Pietrocola

Bibliographic record

VenueJournal of Physics Conference Series · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicRace, Identity, and Education in Brazil
Canadian institutionsConcordia University
Fundersnot available
KeywordsPerspective (graphical)CurriculumMathematics educationSociologyPedagogyEngineering ethicsPsychologyEngineeringComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract The educational process is characterized as a set of pedagogical actions developed for a particular social group and provided with intentionality. In this sense, every pedagogical action involves an intention, which transfers and reproduces cultural patterns that materialize in social values and traditions. In this context, the postmodern decolonial thinking discussed by Quijano (2019), Mignolo and Walsh (2018), and Abdi (2011) are inserted, as well as the epistemological aspects given by Santos (2020). Thus, with this paper, we seek to present a case study developed in Brazil about the colonial influences that manifest themselves in the science curriculum of the State of São Paulo, Brazil, taking Physics Education as the context for analysis. For that, we used as a method of analysis the Discourse Analysis given by the France perspective about the ideological construction of the discourse. As preliminary results, we identified that the absence of an original Brazilian structure for the construction of the analyzed curriculum corroborates the perspectives of a form of colonization characterized by us as at the primary level, that is, epistemic. With the investigation, we hope to contribute to understanding which the cultural lens that act as a colonizing operator on the set of scientific standards and Brazilian scientific representativeness.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
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 head0.020
GPT teacher head0.358
Teacher spread0.338 · 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 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
Published2024
Admission routes1
Has abstractyes

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