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Record W3049627602

Cultivating Ordinary Voices of Dissent: the Challenge for the Social Studies

2020· article· en· W3049627602 on OpenAlexaff
Graham Pike

Bibliographic record

VenueJournal of international social studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsVancouver Island University
Fundersnot available
KeywordsDissentHumanitySociologyIgnoranceEnvironmental ethicsConformityGlobal citizenshipEpistemologySocial scienceSocial psychologyPolitical sciencePsychologyLawPolitics
DOInot available

Abstract

fetched live from OpenAlex

Two broad ideas emerge from reflections on my career in global and international education: first, that my ‘lived experience’ offers both intelligence and ignorance in terms or how I view the world; and second, that the essence of my humanity is enhanced through my identification with, and sense of responsibility for, fellow humans. The latter idea is encapsulated in the African philosophy of  ubuntu.  These two ideas prompt my contention that the global education movement has failed to adequately convey through its literature and practice the complexity and interrelatedness of global systems, including the inextricable connections between humans and their environments. The nature of contemporary global challenges, such as climate change, demands that we understand how global systems are intertwined and adjust our actions accordingly. The social studies need to be at the forefront of nurturing systems level thinking and innovation, particularly to counter the tendency arising from advances in information technology to develop cultures of conformity. Young people around the world have the potential to bring about system-wide change through their ordinary voices of dissent, a collective commitment to decision-making based on recognizing the needs of all humanity, rather than just assessing the benefits to individuals or nations.

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)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.792
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
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 head0.209
GPT teacher head0.495
Teacher spread0.286 · 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 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

Citations1
Published2020
Admission routes1
Has abstractyes

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