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Reading and Writing Connections in Source‐Based Writing

2018· other· en· W2908030757 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe TESOL Encyclopedia of English Language Teaching · 2018
Typeother
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsReading (process)DisciplineAcademic writingContext (archaeology)Raising (metalworking)EthnographyProfessional writingPedagogyMathematics educationPsychologyComputer scienceLinguisticsSociologyEngineering

Abstract

fetched live from OpenAlex

Academic writing is mostly source based, requiring sophisticated skills to connect reading and writing with analytic and critical‐thinking skills. While misuses of source texts are frequently associated with plagiarism, researchers have argued that inappropriate textual borrowing could stem from linguistic, cultural, and developmental challenges for second language students as language learners and novice writers. A pedagogy for source‐based writing is recommended to help students develop various intertextual skills through awareness‐raising discussions and focused skill‐building exercises on paraphrasing, quoting, and summarizing, combined with or in the context of completing meaningful reading and writing tasks. To guide students in writing a typical academic paper that requires their work to be presented in the larger context of academic or disciplinary discourse, an ethnographic approach is suggested. With this approach students, as participant observers, can explore source‐use skills through interviews of experienced writers and corpus analyses of published articles in the relevant disciplinary area.

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.

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)0.005
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.652
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.290
Teacher spread0.279 · 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