MétaCan
Menu
Back to cohort
Record W4389005017 · doi:10.5430/jct.v12n6p347

Content Words and Readability in Students’ Thesis Findings

2023· article· en· W4389005017 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Curriculum and Teaching · 2023
Typearticle
Languageen
FieldComputer Science
TopicText Readability and Simplification
Canadian institutionsnot available
Fundersnot available
KeywordsReadabilityBachelorComputer scienceReading (process)Content (measure theory)Content analysisNatural language processingLinguisticsLexical densityMathematics educationPsychologyLexical itemMathematics

Abstract

fetched live from OpenAlex

This study investigates the content words and readability in bachelor’s thesis findings in the English Literature Program at the University of Sumatera Utara. Qualitative analysis was applied in this study. The data for this study were content words and sentences taken from the data sources of 13 bachelor’s thesis findings. The content words were collected using a lexical density online tool, and the data for readability was collected and analyzed using an online Flesch Reading Ease tool. The results show that the lexical density of the content words ranges from 50.47% – 57.5%. Whilst the readability of the 13 texts range from 19.1 – 61.7. The average score of content word density indicates that the theses’ findings present concise information as represented in scientific writing, and the readability style ranges from "very difficult to read” to “standard readable”. In conclusion, these findings can be categorized as densely written language and content words, supported by college students' increasingly intricate choice of words and sentences frequently read.

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)0.003
metaresearch head score (Gemma)0.000
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 score0.100
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.031
GPT teacher head0.294
Teacher spread0.263 · 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