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Record W2910983995

Rhizo-Autoethnographic Research: critical theories and the understanding of research methods/methodologies in qualitative studies

2018· article· en· W2910983995 on OpenAlexaff
Gustavo Henrique da Cunha Moura

Bibliographic record

Venue2018 Conference of the Canadian Society for the Study of Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsAutoethnographySociologyQualitative researchSituatedPedagogyField (mathematics)ReflexivityEpistemologyContextualizationEngineering ethicsEducational researchDiversity (politics)Social scienceInterpretation (philosophy)EngineeringComputer science
DOInot available

Abstract

fetched live from OpenAlex

Critical theories continue to reshape society in various ways and to enact changes in the education field. Scholars have generated ideas about classroom management, learning and teaching practices, and the curriculum itself, adding to each of these extensions an accurate and situated view of what needs to be worked on. The concept of contextualization has expanded views towards different positions taken by all those who have been involved in and affected by educational institutions and their far-reaching impact on education at all levels from pre-school to post-graduate and adult learning. With the amount of studies deriving from such practices, researchers have dealt with encounters in different places with people from diverse backgrounds. As much as it is an enriching opportunity, the sheer diversity challenges researchers to think of how they would necessarily apply methods and methodologies in the development and analysis of such complex experiences. Through rhizome and autoethnography perspectives, findings suggest that broadening understandings of doing qualitative research can yield not an easier approach for researching but rather a complex experiment in considering researcher positioning outside of conventional discourses.

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)0.043
metaresearch head score (Gemma)0.025
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.525
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0430.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.028
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.888
GPT teacher head0.688
Teacher spread0.200 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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