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Record W3032350196 · doi:10.5430/jct.v9n2p70

The Hidden Curriculum and the Development of Latent skills: The Praxis

2020· article· en· W3032350196 on OpenAlexvenueno aff
Winston Kwame Abroampa

Bibliographic record

VenueJournal of Curriculum and Teaching · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicValues and Moral Education
Canadian institutionsnot available
Fundersnot available
KeywordsPraxisCurriculumHidden curriculumCollateralAccountabilityClass (philosophy)Order (exchange)PsychologyMathematics educationDomain (mathematical analysis)PedagogyHolistic educationEngineering ethicsComputer sciencePolitical scienceEngineeringBusiness

Abstract

fetched live from OpenAlex

The paper sought to explore the extent to which the hidden curriculum also referred to as the collateral curriculum can be used to develop skills, values and attitudes for learners to inculcate in order to develop the affective domain. Primarily, education is supposed to ensure the holistic development of any individual with a balanced development of all the domains. However, current educational policies and their implementation overemphasise the development of intellectual abilities to the detriment of, especially, the affective domain due to narrow and restrictive accountability practices. Since learners learn more than what they are taught in class and what they acquire from the school’s culture stays much longer with them, it is reasonable they are given the opportunity to explore in order to create a school environment and a culture that would effectively evolve such soft skills and affective elements for learners. Various aspects of school life from which affective elements can be practically derived have been discussed with its attendant educational policy implications.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.017
Scholarly communication0.0040.008
Open science0.0010.007
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.310
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2020
Admission routes1
Has abstractyes

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