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Record W1652300414 · doi:10.3968/5279

Power Knowledge Contestations at a Transforming Free State Higher Education Institution: Learning Guide as a Metaphor

2014· article· en· W1652300414 on OpenAlexvenueno aff
Vussy A. Nkonyane

Bibliographic record

VenueHigher education of social science · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSociologyPower (physics)MetaphorEpistemologyDramaState (computer science)Meaning (existential)Relation (database)DisadvantagedContext (archaeology)PoliticsInstitutionSocial realityPolitical scienceSocial scienceLawLinguisticsLiteraturePhilosophyComputer science

Abstract

fetched live from OpenAlex

The purpose of this paper is to examine the connection between power relations and knowledge or truth. Foucault developed the idea of a ‘regime’ of truth. For Foucault, truth/knowledge is linked in a circular relation with systems of power which both produce and sustain it, and also to the effects of power which it induces and which extend it. The status of truth plays an economic political role. Power is produced in (and produces) social relations, and so is closely linked with systems of knowledge or truth; or in other words, with discursive practices. This complex and inevitable drama plays itself out in South African higher education institutions under the context of transformation today. The Author abuses the learning guide as a stage on which the power knowledge relations drama unfolds at two merged historically different institutions of higher learning. Critical emancipatory theory lends itself well to allow a focused gaze on the unfolding engagements between the dominant and privileged group against the dominated, excluded, disadvantaged and marginalized group. The unveiling discourses are given meaning through Textually Oriented Discourse Analysis (TODA).

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.957
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.023
GPT teacher head0.332
Teacher spread0.308 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations3
Published2014
Admission routes1
Has abstractyes

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