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Curriculum or Common Sense? An Exploration in the Classroom Production of Useful Knowledge

2018· article· en· W187787551 on OpenAlexaboutno aff
Peter R. Grahame

Bibliographic record

VenueJournal of educational thought. · 2018
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumCommon senseProduction (economics)PedagogyMathematics educationCurriculum theoryCurriculum developmentPsychologyKnowledge productionSociologyEpistemologyComputer scienceKnowledge managementPhilosophy

Abstract

fetched live from OpenAlex

The relation among instrumental knowledge, practical understanding, and critical insight has been examined in a wide variety of contexts, including workers' study institutes of I 9th century England (Johnson, 1979), the public school in post-World War I middle America (Lynd and Lynd, 1929), and third-world literacy programs (Freire, 1973). A recurrent theme is the tendency of instrumental or technicist concepts of education to suppress those interests and concerns of students which might lead to their developing critical insights into their situation. In other words, certain attempts to make education more practical or useful may, ironically, make students less able to form autonomous judgements about their social situation and more dependent on official definitions of it. This paper explores some problems associated with attempts to implement a curriculum geared to imparting useful knowledge. In particular, it focuses on the relation of practical understanding and critical insight to the instrumental tendencies associated with a recent curriculum innovation, life skills education, which has found wide application in educational and training contexts in Canada. In the pages which follow, I consider some main features of the life skills approach and raise issues connected with the social dimensions of school curricula of this kind. I then turn to a case of teaching skills for everyday living in the setting of a secondary school classroom.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.545
Threshold uncertainty score0.229

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.062
GPT teacher head0.364
Teacher spread0.302 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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