References, Paraphrases and Quotations: Essentials for Writing a Non-plagiarized Text
Bibliographic record
Abstract
This article examines how four students in high school or college choose to integrate sources in their assignments using quotation and paraphrases. Implementing an innovative methodology, a digital screen capture software was used to record all the participants’ actions as they wrote a 500-word argumentative essay. A video of each participant’s actions was produced. These actions translated as quantitative results and showed the frequency of various actions grouped within five categories of strategies linked to various skills (informational skills, writing skills, referencing skills, basic computer skills and task compliance skills) and a sixth category linked to plagiarism actions. The four texts were also analysed for their quality and their level of plagiarism. Results show that the college students performed better on overall text quality, but their texts contained more plagiarism. When looking at the strategies used, all students spent more time on their informational and writing strategies than on their referencing strategies. When using sources, in general, participants had more difficulties with paraphrasing than with quoting, often not referencing their paraphrases, which resulted in plagiarism. Patterns emerged for the data showing four types of actions when integrating sources in assignments: the casual integrator, the aspiring integrator, the fearless integrator and the ethical integrator. For each profile, recommendations on how to better develop students’ paraphrasing, quoting, and referencing skills are provided.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".