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Record W3083051684 · doi:10.5539/jel.v9n5p106

Educational Preferences Among Conservatives and Liberals in the United States: A Quantitative Survey Study

2020· article· en· W3083051684 on OpenAlexvenueno aff
Sandro Sehic

Bibliographic record

VenueJournal of Education and Learning · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsBiology and political orientationPreferenceReading (process)PsychologyPoliticsSocial psychologyOrientation (vector space)Survey researchCognitionPolitical scienceApplied psychologyLaw

Abstract

fetched live from OpenAlex

The purpose of this quantitative survey research study was to explore educational preferences among individuals of conservative and liberal political orientation and of both genders in the United States of America with a 13-questionnaire survey that includes questions relating to different educational preferences. The literature review has revealed previously conducted research study that suggest that individuals of conservative and liberal political orientation may have psychological differences in the domain of emotions, attention, self-control, and cognition. However, the literature review did not reveal research studies that explored educational preferences between individuals with conservative and liberal political orientation in the United States. The results suggest that statistically significant difference exists in the preference to study abroad (χ ² (1, N = 200) = 3.739, p = 0.05). Additional differences, but without a statistically significant differences, were found in the preferences to read fiction and non-fiction genre, perform physically and non-physically challenging activities, perform reading and written assignments, and study in instructional settings where ration between the teachers and technology is uneven.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.137
GPT teacher head0.417
Teacher spread0.280 · 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 designObservational
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
Published2020
Admission routes1
Has abstractyes

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