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Record W6997099173

Towards a Cross-linguistic Analysis of Perception of Tone in Academic Reading Materials

2014· other· en· W6997099173 on OpenAlexaboutno aff

Bibliographic record

VenueArca (British Columbia Electronic Library Network) · 2014
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)Tone (literature)VocabularyPerceptionContext (archaeology)Task (project management)Academic writingExtensive reading
DOInot available

Abstract

fetched live from OpenAlex

1 Introduction:\n\nLanguage learning at best is an arduous process. Coupled with trying to achieve success in academic areas, the task at times might seem insurmountable. Well aware that students might need some relief from the constant onslaught of information, textbook writers at times insert some degree of light heartedness to keep the reader engaged. The concern this study addresses is the problem of language students’ not recognizing tone that might be interpreted as something other than serious in academic reading materials in the English language,and whether or not linguistic group could offer any insight. Effective intercultural communication in a global context has now become imperative as the number of students studying in English internationally has become significant.\n\n2 The problem:\n\nAs many academic instructors can attest, the ability to identify tone in textbook reading often goes undetected by many English Second Language students, thereby reducing the students’ understanding that not all of their educational experience is dull and dry. \n\nLanguage instructors are often perplexed by students’ inability to identify humour as a tone in some reading assignments. Many academic writers \ndo inject a little lightheartedness here and there in order to make their writing a little less dull and hopefully to foster an interest in whatever information it is they are trying to transmit. When students are asked to read a passage or essay and then to identify any parts that seemed humorous - or less than serious-the task is not always possible for everyone. Even when the vocabulary and syntax are relatively simple, the ability to recognize the correct tone is still elusive. Perhaps the problem sometimes rests in such culturally contrasting senses of humour that it is impossible for some students to perceive the language as humorous or entertaining. Sometimes, because of pre-existing cultural schema, humour in a textbook would be completely unexpected. Although a student may still understand the information, not recognizing the author’s effort makes the reading experience just a little bit less enjoyable and the reader perhaps a little less engaged than might be possible. \n\nResearch abounds on many aspects of cross linguistic differences in humour, particularly joke telling (Attardo, 1994). Ample research also exists on how language learning can be facilitated by the incorporation of humour (Bell, 2009). However, there seems to be little information specific to cross linguistic differences regarding tone recognition in academic materials.\n\n3 The study:\n\nThis paper presents an overview of an empirical study done at a Canadian university examining responses from approximately 400 first year university students from 14 different linguistic groups as related to perceived differences of humorous tone in academic textbooks from a range \nof subject areas. The questionnaire used for the study is a hand built corpus of actual passages taken from first year academic textbooks, first piloted on faculty to assure the humour value. The study then analyses the results for specific areas of difference while applying theories of formulaic language to account for some of the problematic items. This study provides some empirical evidence that when learners are not yet highly familiar with the usual contextualized phrases of a language, it is difficult to sense when register variations for the purpose of humour or some other engaging-type language have occurred. This paper proposes the need for much larger collections of humour derived from textbooks. A sizeable natural databank could help teachers give students the skills to better appreciate textbook authors’ intentions. This research is in further development to a paper published in Language and Humour in the Media, Cambridge Scholars, 2012, which applied sociolinguistic schema theory to a prior smaller sampling that addressed both literary and non-literary texts.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.008
GPT teacher head0.267
Teacher spread0.260 · 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 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
Published2014
Admission routes1
Has abstractyes

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