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Record W1908204048 · doi:10.14742/ajet.1319

Contrasting syntactic and semantic units in the analysis of online discussions

2005· article· en· W1908204048 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

VenueAustralasian Journal of Educational Technology · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsComputer scienceParagraphNatural language processingCoding (social sciences)IdentifiabilityAsynchronous communicationArtificial intelligenceInformation retrievalStatisticsWorld Wide WebMachine learningMathematics

Abstract

fetched live from OpenAlex

<span>This paper reports on a study which contrasts results obtained using semantic and syntactic units of analysis in a context of content analysis of an online asynchronous discussion. The paper presents a review of literature on both types of units. The data set consisted of 80 messages posted by ten participants in an online learning module. Data were coded twice by two coders working independently. In the first instance, each coder divided all messages into semantic units and then coded those units. The second coding was conducted on the basis of a syntactic unit of a paragraph. Analysis at the level of the whole group showed little difference in results between the two types of coding. At the level of individual participants, those differences were greater. Results are discussed within a framework of reliability, capability of the unit to discriminate between behaviors, feasibility of different units, and their identifiability. Implications for research are discussed.</span>

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.168

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.024
GPT teacher head0.358
Teacher spread0.334 · 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