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Record W2402594901 · doi:10.5539/elt.v9n6p223

The Effects of Chewing Cinnamon Flavored Gum on Mood, Feeling and Spelling Acquisition

2016· article· en· W2402594901 on OpenAlexvenueno aff
Andrew M. Wilson, Bryan Raudenbush

Bibliographic record

VenueEnglish Language Teaching · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Quality and Safety Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSpellingFeelingMemorizationPsychologyMoodChewing gumRecallRote learningPronunciationTest (biology)Developmental psychologyMathematics educationSocial psychologyTeaching methodCognitive psychologyLinguistics

Abstract

fetched live from OpenAlex

<p>The purpose of the study is to investigate if the effects of chewing cinnamon flavored gum can increase mood, feeling and spelling acquisition. 5th grade students (n=22) at Ilshin elementary school in South Korea served as participants. The same students were required to take 4 spelling tests with 1 given every day over the course of 4 days. For the 1<sup>st</sup> day, students were required to answer pre-questionnaires pertaining to mood and feeling before studying the spelling words. Students were then given 15 minutes to study while using the rote learning techniques to memorize spelling words; however, they were not given any gum. Afterwards, students were required to take the spelling test to determine memorization achievement. Lastly, students were required to retake the post-questionnaires based on mood and feeling again. On days 2-4 the same protocol was performed, however with 5, 10 or 15 minutes of gum chewing. The results indicated that in terms of the test scores, 15 minutes of chewing resulted in better performance than 5 or 10 minutes of chewing. However, there were no significant outcomes related to the mood and feeling scores. Future research should examine the type of information used for the memorization task, since recall vs. recognition tasks may be differentially affected by chewing.</p>

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.007
GPT teacher head0.216
Teacher spread0.209 · 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 designBench or experimental
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

Citations1
Published2016
Admission routes1
Has abstractyes

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