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Record W2337385073 · doi:10.5430/ijhe.v5n2p263

An Attempt for the Exploration of Academicians’ Experiences of the Standard Foreign Language Tests Held in Turkey through Metaphors

2016· article· en· W2337385073 on OpenAlexvenueno aff
Savaş Yeşilyurt

Bibliographic record

VenueInternational Journal of Higher Education · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicEducation Practices and Challenges
Canadian institutionsnot available
FundersAtatürk Üniversitesi
KeywordsSentenceTest (biology)FeelingPsychologyForeign languageMetaphorLinguisticsPerceptionMathematics educationSocial psychology

Abstract

fetched live from OpenAlex

The purpose of this study is to explore academicians’ perceptions and experiences about the public high-stakes Foreign Language Test(s) (YDS, formerly UDS, KPDS, and their counterparts in different times and contexts) used to measure foreign language proficiency in Turkey. For this purpose, data were collected from academicians with different titles, genders, and ages through a survey in which they were asked to complete the sentence “The FLT is like __________ for me because __________” with a one-or-two-word metaphor and a short sentence explaining their reason for the metaphor they choose. Of the over 2600 academicians the survey forms were sent to through an e-mail, 110 filled-in the forms. The data obtained were analyzed verbally and 34 different metaphors were collected. After the analysis of the metaphors, it was drawn that academicians generally have negative feelings and experiences about the foreign language test(s).

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)0.004
metaresearch head score (Gemma)0.006
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.008
Scholarly communication0.0050.005
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.080
GPT teacher head0.390
Teacher spread0.310 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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